CN108985151B - Handwriting model training method, handwritten character recognition method, device, equipment and medium - Google Patents
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Abstract
The invention discloses a handwriting model training method, a handwritten character recognition method, a device, equipment and a medium. The handwriting model training method comprises the following steps: acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into a bidirectional long-and-short term memory neural network for training, updating network parameters of the bidirectional long-and-short term memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model; acquiring and adopting an irregular Chinese character training sample, training to acquire an adjusted Chinese handwriting character recognition model; acquiring and adopting a Chinese character sample to be tested to obtain an error character training sample; and updating network parameters of the Chinese handwritten character recognition model by adopting the error character training sample based on the particle swarm optimization to obtain the target Chinese handwritten character recognition model. By adopting the handwriting model training method, a target Chinese handwriting character recognition model with high recognition rate of the recognized handwriting character can be obtained.
Description
Technical Field
The invention relates to the field of Chinese character recognition, in particular to a handwriting model training method, a handwritten character recognition method, a device, equipment and a medium.
Background
The traditional handwritten word recognition method mostly comprises the steps of binarization processing, character segmentation, feature extraction, support vector machine and the like for recognition, and when the traditional handwritten word recognition method is adopted to recognize comparatively illegible non-standard words (Chinese handwriting), the recognition accuracy is not high, so that the recognition effect is not ideal. The traditional handwritten character recognition method can only recognize standard characters to a great extent, and the accuracy is low when various handwritten characters in actual life are recognized.
Disclosure of Invention
The embodiment of the invention provides a handwriting model training method, a handwriting model training device, handwriting model training equipment and a handwriting model training medium, and aims to solve the problem that the recognition accuracy of current handwritten characters is low.
A handwriting model training method, comprising:
acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into a bidirectional long-and-short term memory neural network for training, updating network parameters of the bidirectional long-and-short term memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model;
acquiring an irregular Chinese character training sample, inputting the irregular Chinese character training sample into the standard Chinese character recognition model for training, updating network parameters of the standard Chinese character recognition model by adopting a particle swarm algorithm, and acquiring an adjusted Chinese handwritten character recognition model;
acquiring a Chinese character sample to be tested, adopting the adjusted Chinese handwriting character recognition model to recognize the Chinese character sample to be tested, acquiring error characters with recognition results not consistent with real results, and taking all the error characters as error character training samples;
inputting the error word training sample into the adjusted Chinese handwritten word recognition model for training, updating and adjusting network parameters of the Chinese handwritten word recognition model by adopting a particle swarm algorithm, and obtaining a target Chinese handwritten word recognition model.
A handwriting model training apparatus, comprising:
the standard Chinese character recognition model acquisition module is used for acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into the bidirectional long-time and short-time memory neural network for training, updating network parameters of the bidirectional long-time and short-time memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model;
the adjusting Chinese handwriting recognition model obtaining module is used for obtaining an irregular Chinese character training sample, inputting the irregular Chinese character training sample into the standard Chinese character recognition model for training, updating network parameters of the standard Chinese character recognition model by adopting a particle swarm algorithm, and obtaining an adjusting Chinese handwriting recognition model;
the error character training sample acquisition module is used for acquiring a Chinese character sample to be tested, adopting the adjusted Chinese handwriting character recognition model to recognize the Chinese character sample to be tested, acquiring error characters with recognition results not consistent with real results, and taking all the error characters as error character training samples;
and the target Chinese handwritten character recognition model acquisition module is used for inputting the error character training sample into the adjusted Chinese handwritten character recognition model for training, updating and adjusting network parameters of the Chinese handwritten character recognition model by adopting a particle swarm algorithm, and acquiring the target Chinese handwritten character recognition model.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the above-mentioned handwriting model training method when executing said computer program.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the handwriting model training method.
The embodiment of the invention also provides a handwritten character recognition method, a handwritten character recognition device, handwritten character recognition equipment and handwritten character recognition media, so as to solve the problem that the current handwritten character recognition accuracy is not high.
A handwritten word recognition method, comprising:
acquiring a Chinese character to be recognized, recognizing the Chinese character to be recognized by adopting a target Chinese handwritten character recognition model, and acquiring an output value of the Chinese character to be recognized in the target Chinese handwritten character recognition model; the target Chinese handwritten character recognition model is obtained by adopting the handwriting model training method;
and acquiring a target probability output value according to the output value and a preset Chinese semantic word library, and acquiring the recognition result of the Chinese character to be recognized based on the target probability output value.
An embodiment of the present invention provides a handwritten character recognition apparatus, including:
the system comprises an output value acquisition module, a target Chinese handwritten character recognition module and a recognition module, wherein the output value acquisition module is used for acquiring Chinese characters to be recognized, recognizing the Chinese characters to be recognized by adopting a target Chinese handwritten character recognition model and acquiring the output value of the Chinese characters to be recognized in the target Chinese handwritten character recognition model; the target Chinese handwritten character recognition model is obtained by adopting the handwriting model training method;
and the recognition result acquisition module is used for acquiring a target probability output value according to the output value and a preset Chinese semantic word library and acquiring a recognition result of the Chinese character to be recognized based on the target probability output value.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the above-mentioned handwritten word recognition method when executing said computer program.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the handwritten word recognition method.
In the handwriting model training method, the device, the equipment and the medium provided by the embodiment of the invention, the standard Chinese character training sample is adopted to train and obtain the standard Chinese character recognition model based on the particle swarm optimization, and the standard Chinese character recognition model has the capability of recognizing standard Chinese handwriting. And then, the standard Chinese character recognition model is updated in an adjusting way through the non-standard Chinese characters, so that the Chinese character handwriting character recognition model obtained after updating can learn deep features of the handwritten Chinese characters in a training and updating way on the premise of having the capability of recognizing standard and standard characters, and the Chinese character handwriting character recognition model can better recognize the handwritten Chinese characters. And then, recognizing the Chinese character sample to be tested by adopting the adjusted Chinese handwriting recognition model, obtaining error characters of which the recognition result is not consistent with the real result, inputting all the error characters serving as error character training samples into the adjusted Chinese handwriting recognition model for training and updating, updating and adjusting the network parameters of the Chinese handwriting recognition model by adopting a particle swarm algorithm, and obtaining the target Chinese handwriting recognition model. The adoption of the error word training sample can further optimize the recognition accuracy rate, and can further reduce the influence of over-learning and over-weakening generated during model training. The training of each model adopts a bidirectional long-time and short-time memory neural network, the neural network can combine the sequence characteristics of Chinese characters, and from the angles of the forward direction of the sequence and the reverse direction of the sequence, the deep characteristics of the Chinese characters are learned, and the function of identifying different Chinese handwritten words is realized. The particle swarm algorithm is adopted when each model updates the network parameters, global random optimization can be carried out through the particle swarm algorithm, the convergence field of the optimal solution can be found in the initial stage of training, then convergence is carried out in the convergence field of the optimal solution, the optimal solution is obtained, the minimum value of the error function is solved, and the network parameters are updated. The particle swarm optimization can obviously improve the efficiency of model training, effectively update network parameters and improve the identification accuracy of the obtained model.
In the handwritten character recognition method, device, equipment and medium provided by the embodiment of the invention, the Chinese character to be recognized is input into the target Chinese handwritten character recognition model for recognition, and the recognition result is obtained by combining the preset Chinese semantic word library. When the target Chinese handwritten character recognition model is adopted to recognize the Chinese handwritten character, an accurate recognition result can be obtained.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a diagram of an application environment of a handwriting model training method according to an embodiment of the invention;
FIG. 2 is a flow chart of a handwriting model training method according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S10 in FIG. 2;
FIG. 4 is another detailed flowchart of step S10 in FIG. 2;
FIG. 5 is a detailed flowchart of step S30 in FIG. 2;
FIG. 6 is a diagram illustrating a handwriting model training apparatus according to an embodiment of the present invention;
FIG. 7 is a flow chart of a handwritten word recognition method in one embodiment of the invention;
FIG. 8 is a schematic diagram of a handwritten character recognition apparatus in an embodiment of the invention;
FIG. 9 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates an application environment of a handwriting model training method provided by an embodiment of the present invention. The application environment of the handwriting model training method comprises a server and a client, wherein the server and the client are connected through a network, the client is equipment capable of performing man-machine interaction with a user and comprises but is not limited to equipment such as a computer, a smart phone and a tablet, and the server can be specifically realized by an independent server or a server cluster consisting of a plurality of servers. The handwriting model training method provided by the embodiment of the invention is applied to a server.
As shown in fig. 2, fig. 2 is a flowchart illustrating a handwriting model training method according to an embodiment of the present invention, where the handwriting model training method includes the following steps:
s10: acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into a bidirectional long-and-short-term memory neural network for training, updating network parameters of the bidirectional long-and-short-term memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model.
The standard Chinese character training sample refers to a training sample obtained according to a standard character (such as a character belonging to a regular script, a song script, an clerical script and the like, and a regular script or a song script is selected as a common font). A Bi-directional Long Short-Term Memory (BILSTM) is a time-recursive neural network used for training data with sequence characteristics from both the sequence forward direction and the sequence reverse direction. The bidirectional long-and-short time memory neural network can not only be associated with the preceding data, but also be associated with the following data, so that deep features of the data related to the sequence can be learned according to the context of the sequence. The data with the sequence characteristics are subjected to neural network model training in a bidirectional long-time and short-time memory mode, and a recognition model corresponding to the data can be obtained. The Particle Swarm Optimization (PSO) is a global random Optimization algorithm, and can find the convergence field of the optimal solution at the initial stage of training, and then converge again in the convergence field of the optimal solution to obtain the optimal solution, i.e. find the minimum value of the error function, and realize the effective update of the network parameters.
In this embodiment, a standard chinese character training sample is obtained, where the training sample is obtained from a standard Chinese character belonging to a chinese character font such as regular script, song script, clerical script, or the like. It should be understood that the standard word herein refers to a word belonging to a mainstream font in a current chinese font, such as a word of a default font song body in an input method of a computer device, a word of a mainstream font regular font commonly used for copying, and the like; characters with Chinese characters, such as cursive script characters and round characters, which are rarely used in daily life, are not included in the standard characters. After the standard Chinese character training sample is obtained, the standard Chinese character training sample is input into the bidirectional long-and-short term memory neural network for training, network parameters of the bidirectional long-and-short term memory neural network are updated by adopting a particle swarm algorithm, and a standard Chinese character recognition model is obtained. The standard Chinese character recognition model learns deep characteristics of a standard Chinese character training sample in a training process, so that the model can accurately recognize standard characters and has the recognition capability of the standard characters. It should be noted that, no matter the standard words corresponding to other Chinese fonts such as regular script, song script, clerical script and the like are adopted in the standard Chinese character training sample, because the standard words are not very different in the aspect of font identification, therefore, the standard Chinese character recognition model can accurately recognize the standard characters corresponding to the characters such as the regular script, the song script, the clerical script and the like, and obtain a more accurate recognition result.
S20: acquiring an irregular Chinese character training sample, inputting the irregular Chinese character training sample into a standard Chinese character recognition model for training, updating network parameters of the standard Chinese character recognition model by adopting a particle swarm algorithm, and acquiring an adjusted Chinese handwritten character recognition model.
The non-standard Chinese character training sample is a training sample obtained according to a handwritten Chinese character, and the handwritten Chinese character can be a character obtained in a handwriting mode according to the font forms of standard characters corresponding to fonts such as a regular script, a song style, an clerical script and the like. It will be appreciated that the non-canonical Chinese training sample differs from the canonical Chinese training sample in that the non-canonical Chinese training sample is obtained from handwritten Chinese, and since it is handwritten, of course contains a variety of different font styles.
In this embodiment, the server obtains an irregular chinese character training sample, where the training sample includes characteristics of a handwritten chinese character, inputs the irregular chinese character training sample into a standard chinese character recognition model for training and adjustment, and updates a network parameter of the standard chinese character recognition model by using a particle swarm algorithm to obtain an adjusted chinese handwritten character recognition model. It will be appreciated that the canonical Chinese character recognition model has the ability to recognize standard canonical Chinese characters, but does not have high recognition accuracy when recognizing handwritten Chinese characters. Therefore, the embodiment trains by adopting the non-standard Chinese character training sample, so that the standard Chinese handwritten character recognition model adjusts parameters in the model on the basis of recognizing the standard characters to obtain the adjusted Chinese handwritten character recognition model. The adjusted Chinese handwriting recognition model learns deep features of handwritten Chinese characters on the basis of the original recognition standard characters, the Chinese handwriting character adjusting recognition model combines deep features of standard characters and handwritten Chinese characters, can effectively recognize the standard characters and the handwritten Chinese characters at the same time, and obtains a recognition result with high accuracy.
When the character recognition is carried out by the bidirectional long-short time memory neural network, judgment is carried out according to the pixel distribution of the character, the handwritten Chinese character in real life is different from the standard character, but the difference is much smaller than the difference of the handwritten Chinese character and the standard character, for example, the difference of the 'I' of the handwritten Chinese character and the 'I' of the standard character is different in pixel distribution, but the difference is much smaller than the difference of the 'I' of the handwritten Chinese character and the 'I' of the standard character. It can be considered that even though there is a certain difference between the handwritten Chinese character and the corresponding standard word, the difference is much smaller than that of the standard word which does not correspond, and therefore, the recognition result can be determined by the most similar (i.e. the difference is the smallest) principle. The Chinese character handwriting character adjusting recognition model is obtained by training a bidirectional long-short-term memory neural network, combines a standard character and deep features of a handwritten Chinese character, and can effectively recognize the handwritten Chinese character according to the deep features.
It should be noted that the order of step S10 and step S20 in this embodiment is not interchangeable, and step S10 is executed first and then step S20 is executed. The method is characterized in that a standard Chinese training sample is adopted to train a bidirectional long-time and short-time memory neural network, so that the obtained standard Chinese character recognition model has good recognition capability, and the standard Chinese character recognition model has an accurate recognition result on standard characters. And the fine tuning of the step S20 is performed on the basis of good recognition capability, so that the adjusted Chinese handwriting recognition model obtained by training can effectively recognize the handwritten Chinese according to the deep features of the learned handwritten Chinese, and the handwritten Chinese recognition model has a relatively accurate recognition result. If step S20 is executed first or only step S20 is executed, because the handwritten Chinese characters have various forms, the features learned by directly training the handwritten Chinese characters cannot well reflect the features of the handwritten Chinese characters, so that the model is "bad" initially, and it is difficult to have an accurate recognition result for the handwritten Chinese character recognition after how to adjust the model. Although everyone has different handwritten Chinese characters, most of the handwritten Chinese characters are similar to standard characters (for example, the handwritten Chinese characters imitate the standard characters). Therefore, the model training according to the standard characters at first is more in line with objective conditions, the effect is better than that of directly performing the model training on the handwritten Chinese characters, corresponding adjustment can be performed under a 'good' model, and the adjusted Chinese handwritten character recognition model with high handwritten Chinese character recognition rate is obtained.
S30: obtaining a Chinese character sample to be tested, adopting an adjusted Chinese character handwriting recognition model to recognize the Chinese character sample to be tested, obtaining error characters of which the recognition results do not accord with the real results, and taking all the error characters as error character training samples.
The Chinese character sample to be tested is a training sample for testing obtained according to the standard characters and the handwritten Chinese characters, and the standard characters adopted in the step are the same as the standard characters used for training in the step S10 (because each character corresponding to the fonts such as regular script, song script and the like is uniquely determined); the handwritten Chinese characters used may be different from the handwritten Chinese characters used in the training in step S20 (the handwritten Chinese characters of different people are not identical, each character corresponding to the handwritten Chinese characters may correspond to a plurality of font forms, and in order to distinguish from the non-standard Chinese character training samples used in the training in step S20 and avoid the situation of over-fitting of model training, the handwritten Chinese characters different from step S20 are generally used in this step).
In this embodiment, the trained adjusted chinese handwriting recognition model is used to recognize a chinese character sample to be tested, where the chinese character sample to be tested includes a standard word and a preset label value (i.e., a real result), as well as a handwritten chinese character and a preset label value thereof. The standard characters and the handwritten Chinese characters can be input into the adjusted Chinese handwritten character recognition model in a mixed mode during training. When the Chinese character sample to be tested is recognized by adopting the adjusted Chinese handwritten character recognition model, the corresponding recognition result is obtained, and all wrong characters of which the recognition result is not consistent with the label value (real result) are taken as the wrong character training samples. The error word training sample reflects that the problem that the recognition precision of the Chinese character handwriting recognition model is insufficient in adjustment, so that the Chinese character handwriting recognition model can be further updated, optimized and adjusted according to the error word training sample in the following process.
Since the recognition accuracy of the adjusted Chinese handwritten character recognition model is actually influenced by the combination of the standard Chinese character training samples and the non-standard Chinese character training samples, on the premise that the network parameters are updated by the standard Chinese character training samples and then the network parameters are updated by the non-standard Chinese character training samples, the obtained adjusted Chinese handwritten character recognition model can be caused to excessively learn the characteristics of the non-standard Chinese character training samples, so that the obtained adjusted Chinese handwritten character recognition model has very high recognition accuracy on the non-standard Chinese character training samples (including handwritten Chinese characters), but the characteristics of the non-standard Chinese character samples are excessively learned, and the recognition accuracy of the handwritten Chinese characters except the non-standard Chinese character training samples is influenced, therefore, the Chinese handwritten character recognition model is recognized by the Chinese character samples to be tested in the step S30, and the excessive learning of the non-standard Chinese character training samples adopted during training can be greatly eliminated. The Chinese handwriting recognition model is adjusted to recognize the Chinese character sample to be tested so as to find out the error generated by over learning, and the error can be reflected by the error word, so that the network parameters of the Chinese handwriting recognition model can be further updated, optimized and adjusted according to the error word.
S40: and inputting the training sample of the error word into the adjusted Chinese handwritten word recognition model for training, updating and adjusting the network parameters of the Chinese handwritten word recognition model by adopting a particle swarm algorithm, and acquiring the target Chinese handwritten word recognition model.
In this embodiment, the training samples of the erroneous character are input to the adjusted chinese handwritten character recognition model for training, and the training samples of the erroneous character reflect the problem of inaccurate recognition when the adjusted chinese handwritten character recognition model recognizes handwritten chinese characters other than the non-standard chinese handwritten character training samples due to the fact that the characteristics of the non-standard chinese character training samples are over-learned when the adjusted chinese handwritten character recognition model is trained. Moreover, due to the fact that the standard Chinese character training samples are adopted firstly and then the non-standard Chinese character training samples are adopted to train the model, the characteristics of the originally learned standard characters can be weakened excessively, and therefore the 'frame' which is initially built by the model and used for identifying the standard characters can be influenced. The problems of over-learning and over-weakening can be well solved by utilizing the error word training sample, and the adverse effects caused by over-learning and over-weakening generated in the original training process can be eliminated to a great extent according to the problems of the error word training sample in the recognition accuracy. Specifically, a particle swarm algorithm is adopted when the training is carried out by adopting the error word training sample, network parameters of the Chinese handwritten character recognition model are updated and adjusted according to the algorithm, and the target Chinese handwritten character recognition model is obtained and is a model which is finally trained and can be used for recognizing the Chinese handwritten character.
It can be understood that the bidirectional long-short term memory neural network used for training each model can be combined with the sequence characteristics of the Chinese characters, and from the angles of the forward direction and the reverse direction of the sequence, the deep features of the Chinese characters can be learned, so that the function of identifying different Chinese handwritten words can be realized.
In the steps S10-S40, the standard Chinese character training samples are adopted for training and obtaining the standard Chinese character recognition model, and then the standard Chinese character recognition model is updated in an adjusting mode through the non-standard Chinese characters, so that the adjusted Chinese handwritten character recognition model obtained after updating can learn the deep features of the handwritten Chinese characters in a training and updating mode on the premise of having the capability of recognizing standard characters, and the adjusted Chinese handwritten character recognition model can better recognize the handwritten Chinese characters. And then, recognizing the Chinese character sample to be tested by adopting the adjusted Chinese handwritten character recognition model, acquiring error characters of which the recognition result is not consistent with the real result, inputting all the error characters serving as error character training samples into the adjusted Chinese handwritten character recognition model for training and updating, and acquiring the target Chinese handwritten character recognition model. By adopting the error word training sample, the adverse effects caused by excessive learning and excessive weakening generated in the original training process can be eliminated to a great extent, and the recognition accuracy can be further optimized. The particle swarm algorithm is adopted for updating the network parameters of each model, global random optimization can be carried out, the convergence field of the optimal solution can be found in the initial stage of training, then convergence is carried out in the convergence field of the optimal solution, the optimal solution is obtained, the minimum value of the error function is solved, and effective network parameter updating is carried out on the bidirectional long-time and short-time memory neural network.
In an embodiment, as shown in fig. 3, in step S10, obtaining a canonical chinese character training sample specifically includes the following steps:
s101: acquiring a pixel value feature matrix of each Chinese character in a Chinese character training sample to be processed, and performing normalization processing on each pixel value in the pixel value feature matrix to acquire a normalized pixel value feature matrix of each Chinese character, wherein the normalization processing formula is
MaxValue is the maximum value of the pixel values in the pixel value characteristic matrix of each Chinese character, minValue is the minimum value of the pixel values in the pixel value characteristic matrix of each Chinese character, x is the pixel value before normalization, and y is the pixel value after normalization. />
The Chinese character training sample to be processed is an initially obtained and unprocessed training sample.
In this embodiment, a pixel value feature matrix of each chinese character in a chinese character training sample to be processed is obtained, where each pixel value feature matrix represents a feature of a corresponding word, and a pixel value represents a feature of a word, and since a word is represented based on two dimensions (generally, a word is represented by an m × n image), a pixel value may be represented by a matrix, that is, a pixel value feature matrix is formed. The computer device can identify the form of the pixel value feature matrix and read the numerical values in the pixel value feature matrix. After the server side obtains the pixel value feature matrix, normalization processing is carried out on each pixel value in the feature matrix by adopting a normalization processing formula, and the normalized pixel value feature is obtained. In this embodiment, each pixel value feature matrix can be compressed within the same range interval by using a normalization processing manner, so that the calculation related to the pixel value feature matrix can be accelerated, and the training efficiency of the character recognition model in the training specification can be improved.
S102: dividing pixel values in the normalized pixel value feature matrix of each Chinese character into two types of pixel values, establishing a binarization pixel value feature matrix of each Chinese character based on the two types of pixel values, and combining the binarization pixel feature matrices of each Chinese character to serve as a standard Chinese character training sample.
In this embodiment, the pixel values in the normalized pixel value feature matrix are divided into two types of pixel values, where the two types of pixel values refer to pixel values that only include a pixel value a or a pixel value B. Specifically, a pixel value greater than or equal to 0.5 in the normalized pixel feature matrix may be taken as 1, and a pixel value less than 0.5 may be taken as 0, and a corresponding binarized pixel value feature matrix may be established, where the original pixel value in the binarized pixel feature matrix only contains 0 or 1. And after the binarization pixel value feature matrix is established, taking Chinese character combinations corresponding to the binarization pixel value feature matrix as standard Chinese character training samples. For example, in an image containing a word, a portion containing a word pixel and a portion containing a blank pixel. The pixel values on a word are typically darker in color, with a "1" in the binary pixel value feature matrix representing a portion of a word pixel and a "0" representing a portion of a blank pixel in the image. It can be understood that the character feature representation can be further simplified by establishing the binary pixel value feature matrix, each Chinese character can be represented and distinguished only by adopting the matrices of 0 and 1, the speed of processing the feature matrix of the character by a computer can be improved, and the training efficiency of the Chinese character recognition model in the training specification can be further improved.
S101-S102 carry out normalization processing on Chinese character training samples to be processed and carry out classification of two types of values, obtain a binarization pixel value feature matrix of each Chinese character, and take characters corresponding to the binarization pixel value feature matrix of each Chinese character as standard Chinese character training samples, so that the time for training the standard Chinese character recognition model can be obviously shortened.
It can be understood that what is input to the bidirectional long-and-short-term memory neural network for training is actually a different binarization pixel feature matrix corresponding to each Chinese character, and the binarization pixel feature matrix of each Chinese character represents each corresponding Chinese character. The Chinese characters are respectively ordered-column characteristics in space, and the characteristics can be reflected in the binary pixel characteristic matrix, so that the binary pixel characteristic matrix of each Chinese character can be trained and learned from the perspective of the front-back correlation of the sequence by adopting a bidirectional long-time memory neural network.
In an embodiment, as shown in fig. 4, in step S10, inputting a standard chinese character training sample into a bidirectional long-and-short term memory neural network for training, updating network parameters of the bidirectional long-and-short term memory neural network by using a particle swarm algorithm, and obtaining a standard chinese character recognition model, specifically including the following steps:
s111: the standard Chinese character training sample is inputted into the bidirectional long-and-short-time memory neural network according to the sequence forward direction to obtain the forward output F o Inputting the standard Chinese character training sample into the bidirectional long-time and short-time memory neural network in sequence and reverse direction to obtain reverse output B o Adding the forward output and the backward output to obtain a forward output T o And is expressed as T o ＝F o +B o 。
The bidirectional long-and-short-term memory neural network model comprises an input layer, an output layer and a hidden layer. The hidden layer comprises an input gate, a forgetting gate, an output gate, a neuron state and a hidden layer output. Forgetting gates determine the information to discard in the state of the neuron. The input gate determines the information to be added in the neuron. The output gate determines the information to be output in the neuron. The neuron state determines the information discarded, added and output by each gate, specifically the weight value connected with each gate. The hidden layer output determines the connection weight of the next layer (hidden layer or output layer) connected to the hidden layer. The network parameters of the bidirectional long-short time memory neural network model refer to weights and bias connected between neurons in the neural network model, the network parameters determine the properties of the network, the network has a memory function on a sequence, and data input into the bidirectional long-short time memory neural network are output correspondingly through calculation processing of the network parameters. The network parameters mentioned in this embodiment take the weight as an example, and the bias is the same as the method for updating the weight at the stage of updating the adjustment, and the bias is not described again.
In this embodiment, the normalized chinese character training sample is input to the bidirectional long-short term memory neural network for training, and the output values of the layers of the network are calculated respectively in the bidirectional long-short term memory neural network through response processing of network parameters, including calculating the output of the normalized chinese character training sample at the input gate, the forgetting gate, the output gate, and the neuron state (also called cell state, and the state of the hidden layer to which the neuron belongs is recorded and represented according to the neuron through a specially set neuron) of the hidden layer, and the hidden layer output. Specifically, three activation functions f (sigmoid), g (tanh) and h (softmax) are adopted for calculating output. The weight result can be converted into a classification result by adopting an activation function, and some nonlinear factors can be added into the neural network, so that the neural network can better solve the more complex problem.
The data received and processed by the neurons in the bidirectional long-short-time memory neural network comprises the following data: input canonical chinese training samples: x, neuronal status: and S. Furthermore, the parameters mentioned below also include: the input of the neuron is denoted by a and the output by b. Subscripts l, phi, and w denote input gate, forgetting gate, and output gate, respectively. t represents the time of day. The weights of the neuron connected with the input gate, the forgetting gate and the output gate are respectively recorded as w cl 、w cφ And w cω 。S c Representing the state of the neuron. I denotes the number of neurons of the input layer, H denotes the number of neurons of the hidden layer, and C denotes the number of neurons corresponding to the neuron state (I denotes the I-th neuron of the input layer, H denotes the H-th neuron of the hidden layer, and C denotes the neuron corresponding to the C-th neuron state).
The input gate receives the input sample (input standard Chinese character training sample)
Output value b of the previous moment t-1 h And the last time neuron state S t-1 c By connecting the input standard Chinese character training sample with the weight w of the input gate il The output value at the previous moment and the weight w of the input gate hl And a weight w connecting the neuron and the input gate cl According to the formula->
Calculating the output of the input gate>
Acting an activation function f on +>
By formula>
A scalar in the interval 0-1 is obtained. This scalar controls the proportion of current information received by the neuron based on a composite determination of the current state and past states.
Forgetting gate to receive sample x at current time i t Output value b at the previous time t-1 h And status data S of the last moment t-1 c Through connecting the input standard Chinese character training sample with the weight w of the forgetting gate iφ The output value at the previous moment of connection and the weight w of the forgetting gate hφ And weight w connecting neuron and forget gate cφ According to the formula
Calculates the output of the forgetting door>
Acting an activation function f on->
Is based on the formula>
A scalar in the interval of 0-1 is obtained, and the scalar controls the proportion of the forgotten past information which is judged by the neuron according to the combination of the current state and the past state.
The neuron receives a sample x at the current time i t Output value b at the previous time t-1 h And status data S of the previous moment t-1 c Weight w of standard Chinese character training sample for connecting neuron and input ic Connecting the neuron with the weight w of the output value at the previous moment hc And output scalars of input gate and forget gate, according to formula
Calculating the neuron state at the current moment>
Wherein, it is based on>
Item (1)
The hidden layer state is represented and is needed when the network parameters are updated.
The output gate receives the sample of the current time
Output value b of last moment t-1 h And the neuron state at the current time->
Through connecting input standard Chinese character training sample and weight w of output gate iw Connecting the output value of the previous moment and the weight w of the output gate hw And the weight w connecting the neuron and the output gate cw According to the formula
The output of the calculation output gate is->
Acting an activation function f on->
The above formula
A scalar is obtained in the interval 0-1.
Hidden layer output
Based on the output of the output gate processed with the activation function->
And neuron state can be determined and formulated as->
And (6) calculating. The output values of each layer of the long-time memory neural network model can be obtained through the calculation of the standard Chinese character training samples among each layer.
According to the above calculation processing procedures, we can calculate the output of each layer in the bidirectional long-and-short-term memory neural network layer by layer and obtain the output value of the final output layer. Since the neural network is bi-directional, the output values include a forward output and a reverse output, respectively, with F o And B o Is represented by (F) o I.e. Forward output, B o Namely Backward output), specifically, a standard Chinese character training sample is forwardly input into a bidirectional long-time and short-time memory neural network according to a sequence to obtain a forward output F o Inputting the standard Chinese character training samples into the bidirectional long-and-short-term memory neural network in sequence and reverse direction to obtain reverse output B o . It will be appreciated that assuming that the feature matrix has N columns, the sequence forward direction represents from column 1 to column N and the sequence reverse direction represents from column N to column 1. Output value of output layer, i.e. forward output T o (i.e. Total output), adding the forward output and the backward output to obtain the forward output T o Is formulated as T o ＝F o +B o . The forward output shows the output obtained after the input standard Chinese character training sample is processed by the response of the network parameters, and the training process can be measured according to the forward output and the real resultThe error caused to update the network parameters in accordance with the error.
S112: constructing an error function according to the forward output and the real result, wherein the expression of the error function is
Where N represents the total number of samples of the canonical Chinese training sample, x i Represents the forward output of the i-th training sample, y i Is represented by the formula i The corresponding real result of the ith training sample.
Wherein the real result, i.e. the objective fact (also called label value), is used to calculate the error from the forward output.
In this embodiment, since the forward output obtained after the bidirectional long-and-short term memory neural network processes the standard chinese character training sample is erroneous with the real result, a corresponding error function may be constructed according to the error, so that the bidirectional long-and-short term memory neural network is trained by using the error function, and the network parameters are updated, so that the updated network parameters can obtain the same or more similar forward output as the real result when processing the input training sample. Specifically, an appropriate error function may be constructed according to actual conditions, and the error function constructed in this embodiment is
The error between the forward output and the real result can be better reflected.
S113: updating network parameters of the bidirectional long-time memory neural network by adopting a particle swarm algorithm according to the error function to obtain a standard Chinese character recognition model, wherein the formula of the particle swarm algorithm comprises a particle position updating formula V i+1 ＝w×V i +c1×rand()×(pbest i -X i )+c2×rand()×(gbest-X i ) And particle velocity position update formula X i+1 ＝X i +V i ，X i ＝(x i1 ,x i2 ,...,x in ) Is the position of the ith particle, n represents the sample dimension of the Chinese character training sample in the specification, X i+1 Is the position of the (i + 1) th particle, V i ＝(v i1 ,v i2 ,...,v in ) Is the velocity of the ith particle, V i+1 Velocity of the i +1 st particle, pbest i ＝(pbest i1 ,pbest i2 ,...,pbest in ) Denotes a local extremum corresponding to the i-th particle, gbest = (gbest) 1 ,gbest 2 ,...,gbest n ) Represents the optimum extremum, w is the inertial bias, c1 is the first learning factor, c2 is the second learning factor, and rand () is [0,1]Any random value of (1).
In one embodiment, according to an error function, updating network parameters of a bidirectional long-and-short-term memory neural network by using a particle swarm algorithm to obtain a standard Chinese character recognition model, wherein the particle swarm algorithm comprises a particle position updating formula (formula 1) and a particle speed position updating formula (formula 2), and is as follows:
V i+1 ＝w×V i +c1×rand()×(pbest i -X i )+c2×rand()×(gbest-X i ) - - - - (equation 1)
X i+1 ＝X i +V i - - - - (equation 2)
Wherein, the sample dimension (i.e. the matrix dimension of the binarization pixel value characteristic matrix corresponding to the sample) of the Chinese character training sample is normalized to n, X i ＝(x i1 ,x i2 ,...,x in ) Is the position of the ith particle, X i+1 Is the position of the (i + 1) th particle, x in A position component representing an nth dimension in a position of an ith particle; v i ＝(v i1 ,v i2 ,...,v in ) Is the velocity of the ith particle, V i+1 Is the velocity of the (i + 1) th particle, v in A velocity component representing an nth dimension of the velocity of the ith particle; pbest i ＝(pbest i1 ,pbest i2 ,...,pbest in ) A local extreme value corresponding to the ith particle; gbest = (gbest) 1 ,gbest 2 ,...,gbest n ) For an optimal extremum (also called global extremum), w is the inertial bias, c1 is the first learning factor, c2 is the second learning factor, c1, c2 are typically set to a constant of 2, rand () is [0,1]Any random value of (1).
It will be appreciated that c1 x rand () controls the step size of the particle going through the optimal position towards the particle. c2 x rand () controls the step size of the particle going through the optimal position to all particles; w is inertial bias, and when the value of w is large, the particle swarm shows strong global optimization capability; when the w value is small, the particle swarm shows strong local optimization capability, and the characteristic is very suitable for network training. Generally, in the initial stage of network training, w is generally set to be large so as to ensure that the network training has enough global optimization capability; in the convergence phase of the training, w is typically set to be small to ensure convergence to the optimal solution.
In the formula (1), the first term on the right side of the formula represents an original speed term; the second term on the right side of the formula represents a cognitive part, mainly considers the influence on the position of a new particle according to the historical optimal position of the particle, and is a self-thinking process; the third term on the right of the formula is the "social" part, and the influence on the position of a new particle is mainly considered according to the optimal positions of all particles. The whole formula (1) reflects an information sharing process. Without the first part, the update of the particle velocity depends only on the optimal position that the particle and all particles experience, and the particle has a strong convergence. The first item on the right side of the formula ensures that the particle swarm has certain global optimization capability and has the function of escaping from extreme values, and on the contrary, if the part is very small, the particle swarm can be converged rapidly. The second term on the right of the formula and the third term on the right of the formula ensure the local convergence of the particle swarm. The particle swarm optimization is a global random optimization algorithm, the calculation formula is adopted to find the convergence field of the optimal solution in the initial stage of training, and then convergence is carried out in the convergence field of the optimal solution to obtain the optimal solution (namely, the minimum value of an error function is solved).
The process of updating the network parameters of the bidirectional long-time memory neural network by adopting the particle swarm optimization specifically comprises the following steps:
(1) Initializing the particle position X and the particle velocity V and setting the particle position maximum X max And minimum value X min Maximum value of particle velocity V max And a minimum value V min Inertia weight w, first learning factor c1, second learning factor c2, maximum training timeNumber α, stop iteration threshold ε.
(2) For each particle pbest: calculating the adaptive value of the particle by using an error function (namely finding a more optimal solution), and if the particle finds the more optimal solution, updating pbest; otherwise, pbest remains unchanged.
(3) And comparing the particle with the minimum adaptive value in the local extreme value pbest with the particle adaptive value of the global extreme value gbest, and selecting the particle with the minimum adaptive value to update the value of the gbest.
(4) The particle position X and the particle velocity V of the particle group are updated according to equation (1).
Determine if the speed in pbest exceeds [ V ] min ，V max ]If the speed range is exceeded, the minimum and/or maximum speed is set accordingly.
Determine if the speed in pbest exceeds [ X ] min ，X max ]If the position is out of the range of the position, the inertia weight w is updated, and the formula of the updated inertia weight w is
Beta refers to the current number of training sessions.
(5) Judging whether the maximum training times alpha are reached or the error is smaller than a stop iteration threshold epsilon, and if so, terminating; if not, the steering (2) continues to operate until the requirement is met.
Steps S111-S113 can construct an error function according to the forward output obtained by the normalized Chinese character training sample by memorizing the neural network in two-way long and short time
And according to the error function. The particle swarm algorithm is adopted to update the network parameters, a standard Chinese character recognition model can be obtained, deep features of a standard Chinese character training sample are learned by the model, and standard characters can be recognized accurately.
The step S113 is referred to in the step S20 and the step S40 of updating the network parameters by using the particle swarm algorithm, and is not repeated herein to avoid repetition.
In an embodiment, as shown in fig. 5, in step S30, the method includes the following steps of recognizing a Chinese character sample to be tested by using a Chinese handwritten character recognition model, obtaining an error word with a recognition result not matching with a real result, and using all the error words as error word training samples:
s31: and inputting the Chinese character sample to be tested into the adjusted Chinese handwritten character recognition model, and acquiring the output value of each character in the Chinese character sample to be tested in the adjusted Chinese handwritten character recognition model.
In this embodiment, the Chinese handwriting recognition model is adjusted to recognize a Chinese character sample to be tested, where the Chinese character sample to be tested includes a plurality of Chinese characters. In the Chinese character library, the number of commonly used Chinese characters is about three thousand, a probability value of the similarity degree of each character in the Chinese character library and an input Chinese character sample to be tested is set in an output layer for adjusting the Chinese character recognition model, the probability value is an output value of each character in the Chinese character sample to be tested in the Chinese character recognition model, and the probability value can be realized through a softmax function. In short, when inputting the word "me", the output value (expressed by probability) corresponding to each word in the Chinese character library is obtained in adjusting the Chinese character handwriting recognition model, for example, the output value corresponding to "me" in the Chinese character library is 99.5%, and the output values of the rest of the words are added up to 0.5%. By setting the Chinese character sample to be tested, a reasonable recognition result can be obtained according to the output value after the Chinese handwritten character recognition model is adjusted to recognize the output value corresponding to each character in the Chinese character library.
S32: and selecting the maximum output value of the output values corresponding to each word, and acquiring the recognition result of each word according to the maximum output value.
In this embodiment, the maximum output value of all the output values corresponding to each word is selected, and the recognition result of the word can be obtained according to the maximum output value. It can be understood that the output value directly reflects the similarity degree between the input word in the Chinese character sample to be tested and each word in the Chinese character library, and the maximum output value indicates that the word sample to be tested is closest to a certain word in the Chinese character library, and the word corresponding to the maximum output value can be the recognition result of the word, for example, the recognition result output at the end of inputting the word "i" is "i".
S33: and acquiring error words with the recognition result not consistent with the real result according to the recognition result, and taking all the error words as error word training samples.
In this embodiment, the obtained recognition result is compared with a real result (objective fact), and an error word whose comparison recognition result does not match the real result is used as an error word training sample. It can be understood that the recognition result is only the result of the Chinese character training sample to be tested in the process of adjusting the Chinese handwritten character recognition model, and is possibly different from the real result, which reflects that the model still has defects in recognition accuracy, and the defects can be optimized through the error character training sample to achieve more accurate recognition effect.
S31-S33, according to the output value of each character in the Chinese handwritten character recognition model in the Chinese character sample to be tested, selecting the maximum output value capable of reflecting the similarity degree between the characters from the output values; and obtaining a recognition result through the maximum output value, and obtaining an error word training sample according to the recognition result, thereby providing an important technical premise for further optimizing the recognition accuracy by using the error word training sample.
In an embodiment, before step S10, that is, before the step of obtaining the training samples of the normative Chinese characters, the handwriting model training method further includes the following steps: and initializing a bidirectional long-time and short-time memory neural network.
In one embodiment, initializing the bi-directional long and short term memory neural network initializes the network parameters of the network and assigns initial values to the network parameters. If the initialized weight is in a relatively gentle area of the error curved surface, the convergence speed of the bidirectional long-and-short-term memory neural network model training may be abnormally slow. The network parameters may be initialized to be evenly distributed within a relatively small interval having a mean value of 0, such as an interval of [ -0.30, +0.30 ]. The method has the advantages that the two-way long-and-short time memory neural network is initialized reasonably, so that the network has flexible adjusting capacity in the initial stage, the network can be adjusted effectively in the training process, the minimum value of the error function can be found quickly and effectively, the updating and adjusting of the two-way long-and-short time memory neural network are facilitated, and the model obtained by model training based on the two-way long-and-short time memory neural network has an accurate recognition effect when the Chinese handwriting is recognized.
In the handwriting model training method provided in this embodiment, the network parameters of the two-way long-short time memory neural network are initialized to be uniformly distributed in a relatively small interval with a 0-mean value, such as [ -0.30, +0.30 [)]In such an interval, the minimum value of the error function can be quickly and effectively found by adopting the initialization mode, and the updating and the adjustment of the bidirectional long-time and short-time memory neural network are facilitated. The Chinese character training sample to be processed is normalized and divided into two types of values, a binarization pixel value characteristic matrix of each character is obtained, the character corresponding to the characteristic matrix is used as a standard Chinese character training sample, and the time length for training the standard Chinese character recognition model can be obviously shortened. Constructing an error function according to the forward output obtained by memorizing a neural network in two-way long and short time according to a standard Chinese character training sample
And the network parameters are updated according to the error function in a back-propagation mode, so that a standard Chinese character recognition model can be obtained, deep features of a standard Chinese character training sample are learned by the model, and standard characters can be recognized accurately. And then, the standard Chinese character recognition model is updated in an adjusting way through the non-standard Chinese characters, so that the deep features of the non-standard Chinese characters can be learned through a training and updating way on the premise that the updated adjusted Chinese character recognition model has the capacity of recognizing the standard Chinese characters, and the adjusted Chinese character recognition model can better recognize the non-standard Chinese characters. Then, according to the output value of every character in the Chinese character sample to be tested in the Chinese hand-written character recognition model regulation, selecting maximum output value capable of reflecting similarity degree between characters from output values, utilizing maximum output value to obtain recognition result, according to the recognition result obtaining error character training sample, and inputting all error characters as error character training sample into the regulationAnd performing training and updating in the whole Chinese handwritten character recognition model to obtain a target Chinese handwritten character recognition model. By adopting the error word training sample, the adverse effects caused by over-learning and over-weakening generated in the original training process can be eliminated to a great extent, and the recognition accuracy can be further optimized. In addition, in the handwriting model training method provided by this embodiment, each model is trained by using a bidirectional long-and-short-term memory neural network, and the neural network can learn deep features of a word from the angles of the forward direction and the reverse direction of the sequence by combining sequence characteristics of the word, so as to realize the function of identifying different Chinese handwriting; the particle swarm algorithm is adopted when each model updates the network parameters, the algorithm can carry out global random optimization, the convergence field of the optimal solution can be found in the initial stage of training, then convergence is carried out in the convergence field of the optimal solution, the optimal solution is obtained, the minimum value of an error function is solved, and the network parameters are updated. The particle swarm algorithm can obviously improve the efficiency of model training, effectively update network parameters and improve the identification accuracy of the obtained model.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 6 is a schematic block diagram of a handwriting model training apparatus corresponding to the handwriting model training method in one-to-one embodiment. As shown in fig. 6, the handwriting model training apparatus includes a standard chinese character recognition model obtaining module 10, an adjusted chinese character recognition model obtaining module 20, an erroneous character training sample obtaining module 30, and a target chinese character recognition model obtaining module 40. The implementation functions of the standard Chinese character recognition model obtaining module 10, the adjusted Chinese handwritten character recognition model obtaining module 20, the error character training sample obtaining module 30, and the target Chinese handwritten character recognition model obtaining module 40 correspond to the steps corresponding to the handwriting model training method in the embodiment one to one, and for avoiding redundancy, detailed description is not provided in this embodiment.
And the standard Chinese character recognition model acquisition module 10 is used for acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into the bidirectional long-and-short term memory neural network for training, updating network parameters of the bidirectional long-and-short term memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model.
And the adjusted Chinese handwriting recognition model obtaining module 20 is used for obtaining the non-standard Chinese character training sample, inputting the non-standard Chinese character training sample into the standard Chinese character recognition model for training, updating the network parameters of the standard Chinese character recognition model by adopting a particle swarm algorithm, and obtaining the adjusted Chinese handwriting recognition model.
The error character training sample obtaining module 30 is configured to obtain a Chinese character sample to be tested, identify the Chinese character sample to be tested by using the adjusted Chinese character handwriting recognition model, obtain error characters with recognition results not matching the real results, and use all the error characters as error character training samples.
And the target Chinese handwritten character recognition model acquisition module 40 is used for inputting the error character training samples into the adjusted Chinese handwritten character recognition model for training, updating and adjusting network parameters of the Chinese handwritten character recognition model by adopting a particle swarm algorithm, and acquiring the target Chinese handwritten character recognition model.
Preferably, the canonical Chinese character recognition model obtaining module 10 includes a normalized pixel value feature matrix obtaining unit 101, a canonical Chinese character training sample obtaining unit 102, a forward output obtaining unit 111, an error function constructing unit 112, and a canonical Chinese character recognition model obtaining unit 113.
A normalized pixel value feature matrix obtaining unit 101, configured to obtain a pixel value feature matrix of each Chinese character in a Chinese character training sample to be processed, perform normalization processing on each pixel value in the pixel value feature matrix, and obtain a normalized pixel value feature matrix of each Chinese character, where a formula of the normalization processing is
Maxvalue is the maximum value of pixel values in the pixel value feature matrix of each Chinese character, minvalue is the maximum value of pixel values in the pixel value feature matrix of each Chinese characterX is the pixel value before normalization and y is the pixel value after normalization.
The canonical Chinese character training sample obtaining unit 102 is configured to divide pixel values in the normalized pixel value feature matrix of each Chinese character into two types of pixel values, establish a binarization pixel value feature matrix of each Chinese character based on the two types of pixel values, and combine the binarization pixel value feature matrices of each Chinese character as a canonical Chinese character training sample.
A forward output obtaining unit 111, configured to forward input the standard Chinese character training samples into the bidirectional long-and-short term memory neural network according to the sequence to obtain a forward output F o Inputting the standard Chinese character training samples into the bidirectional long-and-short-term memory neural network in sequence and reverse direction to obtain reverse output B o Adding the forward output and the backward output to obtain a forward output T o And is expressed as T o ＝F o +B o 。
An error function constructing unit 112 for constructing an error function based on the forward output and the real result, the expression of the error function being
Where N represents the total number of training samples, x i Representing the forward output of the i-th training sample, y i Is represented by the formula i The corresponding real result of the ith training sample.
A normalized Chinese character recognition model obtaining unit 113, configured to obtain a normalized Chinese character recognition model by updating network parameters of the bidirectional long-term and short-term memory neural network with a particle swarm algorithm according to the error function, where the formula of the particle swarm algorithm includes a particle position update formula V i+1 ＝w×V i +c1×rand()×(pbest i -X i )+c2×rand()×(gbest-X i ) And particle velocity position update formula X i+1 ＝X i +V i ，X i ＝(x i1 ,x i2 ,...,x in ) Is the position of the ith particle, n represents the sample dimension of the Chinese character training sample in the specification, X i+1 Is the bit of the (i + 1) th particlePut in, V i ＝(v i1 ,v i2 ,...,v in ) Is the velocity of the ith particle, V i+1 Velocity of the i +1 st particle, pbest i ＝(pbest i1 ,pbest i2 ,...,pbest in ) Denotes a local extremum corresponding to the i-th particle, gbest = (gbest) 1 ,gbest 2 ,...,gbest n ) Represents the optimum extremum, w is the inertial bias, c1 is the first learning factor, c2 is the second learning factor, and rand () is [0,1]Any random value of (1).
Preferably, the error word training sample acquisition module 30 includes a model output value acquisition unit 31, a model recognition result acquisition unit 32, and an error word training sample acquisition unit 33.
The model output value obtaining unit 31 is configured to input the Chinese character sample to be tested to the adjusted Chinese handwritten character recognition model, and obtain an output value of each character in the Chinese character sample to be tested in the adjusted Chinese handwritten character recognition model.
The model identification result obtaining unit 32 is configured to select a maximum output value of the output values corresponding to each word, and obtain an identification result of each word according to the maximum output value.
And an error word training sample obtaining unit 33, configured to obtain, according to the recognition result, an error word whose recognition result does not match the real result, and use all the error words as error word training samples.
Preferably, the handwriting model training device further comprises an initialization module 50, configured to initialize the bidirectional long-time and short-time memory neural network.
Fig. 7 shows a flowchart of the handwritten word recognition method in the present embodiment. The handwritten character recognition method can be applied to computer equipment configured by organizations such as banks, investments, insurance and the like, and is used for recognizing handwritten Chinese characters to achieve the purpose of artificial intelligence. As shown in fig. 7, the handwritten word recognition method includes the steps of:
s50: acquiring a Chinese character to be recognized, recognizing the Chinese character to be recognized by adopting a target Chinese handwritten character recognition model, and acquiring an output value of the Chinese character to be recognized in the target Chinese handwritten character recognition model, wherein the target Chinese handwritten character recognition model is acquired by adopting the handwriting model training method.
Wherein, the Chinese character to be recognized refers to the Chinese character to be recognized.
In this embodiment, a Chinese character to be recognized is obtained, the Chinese character to be recognized is input into the target Chinese handwritten character recognition model for recognition, an output value of the Chinese character to be recognized in the target Chinese handwritten character recognition model is obtained, one Chinese character to be recognized corresponds to more than three thousand output values (the specific number is based on the Chinese character library), and a recognition result of the Chinese character to be recognized can be determined based on the output value. Specifically, the Chinese character to be recognized is specifically represented by a binary pixel value feature matrix which can be directly recognized by a computer.
S60: and acquiring a target probability output value according to the output value and a preset Chinese semantic word library, and acquiring a recognition result of the Chinese character to be recognized based on the target probability output value.
The preset Chinese semantic word bank refers to a preset word bank which describes semantic relations among Chinese words based on word frequency. For example, in the Chinese semantic word library, for the words of two words such as "X Yang", the probability of occurrence of "Sun" is 30.5%, the probability of occurrence of "Dayang" is 0.5%, and the sum of the probabilities of occurrence of the remaining words of two words such as "X Yang" is 69%. The target probability output value is a probability value for acquiring a recognition result of the Chinese character to be recognized by combining the output value and a preset Chinese semantic word library.
Specifically, the step of obtaining the target probability output value by using the output value and a preset Chinese semantic word library comprises the following steps: (1) And selecting the maximum value in the output values corresponding to each character in the Chinese characters to be recognized as a first probability value, and acquiring the primary recognition result of the Chinese characters to be recognized according to the first probability value. (2) And acquiring the left semantic probability value and the right semantic probability value of the word to be identified according to the primary identification result and the Chinese semantic word library. It is understood that for a text, the words in the text are sequential, such as "red X positive", and for the "X" word, there are probability values corresponding to the words "red X" and "X positive" in the left-to-right directions, i.e., left semantic probability value and right semantic probability value. (3) And respectively setting a weight of an output value corresponding to each word in the Chinese characters to be recognized, a weight of a left semantic probability value and a weight of a right semantic probability value. Specifically, a weight of 0.4 may be assigned to an output value corresponding to each word in the Chinese characters to be recognized, a weight of 0.3 may be assigned to the left semantic probability value, and a weight of 0.3 may be assigned to the right semantic probability value. (4) And multiplying the set weights by the corresponding probability values respectively to obtain the probability values after the weighted operation, adding the probability values after the weighted operation to obtain target probability output values (the target probability output values are multiple, and the specific number can be based on a Chinese character library), and selecting a character corresponding to the maximum value in the target probability output values as the recognition result of the Chinese character to be recognized. In fact, the first 5 probability values with the largest value in the output values can be selected first, the first 5 probability values represent the most possible 5 characters (recognition results), and only the 5 characters are combined with the Chinese semantic word library to calculate the target probability output value, so that only 5 target probability output values are obtained, and the recognition efficiency can be greatly improved. By combining the output value with a preset Chinese semantic word library, an accurate recognition result can be obtained. It can be understood that for the recognition of a single word (non-text), the corresponding recognition result can be directly obtained according to the maximum value in the output values, and the recognition based on Chinese semantics is not required to be added.
And S50-S60, recognizing the Chinese character to be recognized by adopting the target Chinese handwritten character recognition model, and acquiring a recognition result of the Chinese character to be recognized by combining the output value and a preset Chinese semantic word bank. The target Chinese handwritten character recognition model has high recognition accuracy and is combined with a Chinese semantic word library to further improve the recognition accuracy of Chinese handwritten characters.
In the handwritten character recognition method provided by the embodiment of the invention, the Chinese character to be recognized is input into the target Chinese handwritten character recognition model for recognition, and a recognition result is obtained by combining a preset Chinese semantic word library. When the target Chinese handwritten character recognition model is adopted to recognize the Chinese handwritten character, an accurate recognition result can be obtained.
Fig. 8 shows a functional block diagram of a handwritten word recognition apparatus in one-to-one correspondence with the handwritten word recognition method in the embodiment. As shown in fig. 8, the handwritten word recognition apparatus includes an output value acquisition module 60 and a recognition result acquisition module 70. The implementation functions of the output value obtaining module 60 and the recognition result obtaining module 70 correspond to the steps corresponding to the handwritten word recognition method in the embodiment one by one, and for avoiding redundancy, a detailed description is not provided in this embodiment.
The handwritten character recognition device comprises an output value acquisition module 60, which is used for acquiring Chinese characters to be recognized, recognizing the Chinese characters to be recognized by adopting a target Chinese handwritten character recognition model and acquiring the output values of the Chinese characters to be recognized in the target Chinese handwritten character recognition model; the target Chinese handwritten character recognition model is obtained by adopting a handwriting model training method.
And the recognition result obtaining module 70 is configured to obtain a target probability output value according to the output value and a preset chinese semantic word library, and obtain a recognition result of the chinese character to be recognized based on the target probability output value.
The present embodiment provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the handwriting model training method in the embodiments is implemented, and for avoiding repetition, details are not described here again. Alternatively, the computer program, when executed by the processor, implements the functions of each module/unit of the handwriting model training apparatus in the embodiments, and is not described herein again to avoid redundancy. Alternatively, the computer program is executed by the processor to implement the functions of the steps in the handwritten character recognition method in the embodiments, and is not repeated here to avoid repetition. Alternatively, the computer program is executed by the processor to implement the functions of the modules/units in the handwritten character recognition apparatus in the embodiments, which are not described herein again to avoid repetition.
Fig. 9 is a schematic diagram of a computer device provided by an embodiment of the invention. As shown in fig. 9, the computer device 80 of this embodiment includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and capable of running on the processor 81, where the computer program 83 is executed by the processor 81 to implement the handwriting model training method in the embodiment, and details are not repeated herein to avoid repetition. Alternatively, the computer program is executed by the processor 81 to implement the functions of each model/unit in the handwriting model training apparatus in the embodiment, which are not repeated herein to avoid repetition. Alternatively, the computer program is executed by the processor 81 to implement the functions of the steps in the handwritten character recognition method in the embodiments, and is not repeated here to avoid repetition. Alternatively, the computer program realizes the functions of each module/unit in the handwritten word recognition apparatus in the embodiments when executed by the processor 81. To avoid repetition, it is not repeated herein.
The computing device 80 may be a desktop computer, a notebook, a palm top computer, a cloud server, or other computing device. The computer device may include, but is not limited to, a processor 81, a memory 82. Those skilled in the art will appreciate that fig. 9 is merely an example of a computing device 80 and is not intended to limit computing device 80 and may include more or fewer components than shown, or some of the components may be combined, or different components, e.g., a computing device may also include input output devices, network access devices, buses, etc.
The Processor 81 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 82 may be an internal storage unit of the computer device 80, such as a hard disk or a memory of the computer device 80. The memory 82 may also be an external storage device of the computer device 80, such as a plug-in hard disk provided on the computer device 80, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 82 may also include both internal storage units and external storage devices of the computer device 80. The memory 82 is used to store computer programs and other programs and data required by the computer device. The memory 82 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A handwriting model training method, comprising:
acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into a bidirectional long-and-short term memory neural network for training, updating network parameters of the bidirectional long-and-short term memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model;
acquiring an irregular Chinese character training sample, inputting the irregular Chinese character training sample into the standard Chinese character recognition model for training, updating network parameters of the standard Chinese character recognition model by adopting a particle swarm algorithm, and acquiring an adjusted Chinese handwritten character recognition model;
acquiring a Chinese character sample to be tested, adopting the adjusted Chinese handwriting character recognition model to recognize the Chinese character sample to be tested, acquiring error characters with recognition results not consistent with real results, and taking all the error characters as error character training samples;
inputting the error word training sample into the adjusted Chinese handwritten word recognition model for training, updating and adjusting network parameters of the Chinese handwritten word recognition model by adopting a particle swarm algorithm, and obtaining a target Chinese handwritten word recognition model.
2. The handwriting model training method according to claim 1, wherein said obtaining canonical chinese character training samples comprises:
acquiring a pixel value feature matrix of each Chinese character in a Chinese character training sample to be processed, and performing normalization processing on each pixel value in the pixel value feature matrix to acquire a normalized pixel value feature matrix of each Chinese character, wherein the normalization processing formula is
MaxValue is the maximum value of the pixel values in the pixel value characteristic matrix of each Chinese character, minValue is the minimum value of the pixel values in the pixel value characteristic matrix of each Chinese character, x is the pixel value before normalization, and y is the pixel value after normalization;
dividing pixel values in the normalized pixel value feature matrix of each Chinese character into two types of pixel values, establishing a binarization pixel value feature matrix of each Chinese character based on the two types of pixel values, and combining the binarization pixel feature matrices of each Chinese character to serve as a standard Chinese character training sample.
3. The method for training a handwritten model according to claim 1, wherein the step of inputting the canonical Chinese character training samples into a bidirectional long-short term memory neural network for training, updating network parameters of the bidirectional long-short term memory neural network by using a particle swarm algorithm, and obtaining a canonical Chinese character recognition model comprises the steps of:
the standard Chinese character training samples are input into a bidirectional long-time and short-time memory neural network in a sequence forward direction to obtain a forward output F o Inputting the standard Chinese character training samples into a bidirectional long-short-time memory neural network in sequence and reversely to obtain a reverse output B o Adding the forward output and the backward output to obtain a forward output T o And is expressed as T o ＝F o +B o ；
Constructing an error function according to the forward output and the real result, wherein the expression of the error function is
Where N represents the total number of training samples, x i Representing the forward output of the i-th training sample, y i Is represented by the formula i The real result of the corresponding ith training sample;
updating network parameters of the bidirectional long-time memory neural network by adopting a particle swarm algorithm according to the error function to obtain a standard Chinese character recognition model, wherein the formula of the particle swarm algorithm comprises a particle position updating formula V i+1 ＝w×V i +c1×rand()×(pbest i -X i )+c2×rand()×(gbest-X i ) And particle velocity position update formula X i+1 ＝X i +V i ，X i ＝(x i1 ,x i2 ,...,x in ) Is the position of the ith particle, n represents the sample dimension of the Chinese character training sample in the specification, X i+1 Is the position of the (i + 1) th particle, V i ＝(v i1 ,v i2 ,...,v in ) Is the speed of the ith particle, n represents the sample dimension of the canonical Chinese training sample, V i+1 The velocity of the (i + 1) th particle,
pbest i ＝(pbest i1 ,pbest i2 ,...,pbest in ) The local extreme value corresponding to the ith particle is shown,
gbest＝(gbest 1 ,gbest 2 ,...,gbest n ) Represents the optimum extremum, w is the inertial bias, c1 is the first learning factor, c2 is the second learning factor, and rand () is [0,1]Any random value of (1).
4. The handwriting model training method of claim 1, wherein said recognizing a Chinese character sample to be tested by using the adjusted Chinese handwriting recognition model, obtaining an error word whose recognition result does not match the true result, and using all the error words as error word training samples comprises:
inputting a Chinese character sample to be tested into an adjusted Chinese handwritten character recognition model, and acquiring an output value of each character in the Chinese character sample to be tested in the adjusted Chinese handwritten character recognition model;
selecting the maximum output value of the output values corresponding to each word, and acquiring the recognition result of each word according to the maximum output value;
and acquiring error words with the recognition result not consistent with the real result according to the recognition result, and taking all the error words as error word training samples.
5. The handwriting model training method according to claim 1, wherein before said step of obtaining canonical chinese character training samples, said handwriting model training method further comprises:
and initializing a bidirectional long-time memory neural network.
6. A method for handwritten word recognition, comprising:
acquiring a Chinese character to be recognized, recognizing the Chinese character to be recognized by adopting a target Chinese handwritten character recognition model, and acquiring an output value of the Chinese character to be recognized in the target Chinese handwritten character recognition model; the target Chinese handwritten character recognition model is obtained by adopting the handwriting model training method of any one of claims 1 to 5;
and acquiring a target probability output value according to the output value and a preset Chinese semantic word bank, and acquiring the recognition result of the Chinese character to be recognized based on the target probability output value.
7. A handwriting model training apparatus, comprising:
the standard Chinese character recognition model acquisition module is used for acquiring a standard Chinese character training sample, inputting the standard Chinese character training sample into the bidirectional long-time and short-time memory neural network for training, updating network parameters of the bidirectional long-time and short-time memory neural network by adopting a particle swarm algorithm, and acquiring a standard Chinese character recognition model;
the adjusting Chinese handwriting character recognition model obtaining module is used for obtaining an irregular Chinese character training sample, inputting the irregular Chinese character training sample into the regular Chinese character recognition model for training, updating network parameters of the regular Chinese character recognition model by adopting a particle swarm algorithm, and obtaining an adjusting Chinese handwriting character recognition model;
the error word training sample acquisition module is used for acquiring a Chinese character sample to be tested, adopting the adjusted Chinese handwritten character recognition model to recognize the Chinese character sample to be tested, acquiring error words with recognition results not consistent with real results, and taking all the error words as error word training samples;
and the target Chinese handwritten character recognition model acquisition module is used for inputting the error character training sample into the adjusted Chinese handwritten character recognition model for training, updating and adjusting network parameters of the Chinese handwritten character recognition model by adopting a particle swarm algorithm, and acquiring the target Chinese handwritten character recognition model.
8. A handwritten word recognition apparatus, comprising:
the system comprises an output value acquisition module, a target Chinese handwritten character recognition module and a recognition module, wherein the output value acquisition module is used for acquiring Chinese characters to be recognized, recognizing the Chinese characters to be recognized by adopting a target Chinese handwritten character recognition model and acquiring the output value of the Chinese characters to be recognized in the target Chinese handwritten character recognition model; the target Chinese handwritten character recognition model is obtained by adopting the handwriting model training method of any one of claims 1-5;
and the recognition result acquisition module is used for acquiring a target probability output value according to the output value and a preset Chinese semantic word bank and acquiring a recognition result of the Chinese character to be recognized based on the target probability output value.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the handwriting model training method according to any of claims 1 to 5 when executing the computer program; alternatively, the processor realizes the steps of the method for handwriting recognition according to claim 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the handwriting model training method according to any one of claims 1 to 5; alternatively, the processor realizes the steps of the method for handwriting recognition according to claim 6 when executing the computer program.
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| JP2009048641A (en) * | 2007-08-20 | 2009-03-05 | Fujitsu Ltd | Character recognition method and character recognition apparatus |
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