Handwritten Character Recognition

Research in image processing involves many active areas, of these Recognition of Handwritten character holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize intelligibly hand written inputs In this paper, a new method that uses structural features and support vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

We present a framework for handwritten Bangla digit recognition using Sparse Representation Classifier. The classifier assumes that a test sample can be represented as a linear combination of the train samples from its native class. Hence, a test sample can be rep- resented using a dictionary constructed from the train samples. The most sparse linear representation of the test sample in terms of this dictionary can be efficiently computed through l1-minimization, and can be exploited to classify the test sample. We applied Sparse Representation Classifier on the image zone density, an image domain statistical feature extracted from the character image, to classify the Bangla numerals. This is a novel approach for Bangla Optical Character Recognition, and demonstrates an excellent accuracy of 94% on the off-line handwritten Bangla numeral database CMATERdb 3.1.1. This result is promising, and should be investigated further.

Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental tool to generate feature descriptors where the Zernike moment technique has a rotation invariance property which found to be desirable for handwritten character recognition. This paper discusses extraction of features from handwritten compound characters using Zernike moment feature descriptor and proposes SVM and k-NN based classification system. The proposed classification system preprocess and normalize the 27000 handwritten character images into 30×30 pixels images and divides them into zones. The pre-classification produces three classes depending on presence or absence of vertical bar. Furt...

In this new informative era, data and information is the most important asset to organizations. A large amount of money and manpower have been spent in data gathering, data entry, and storage every year. In Malaysia, data gathering is still largely done through manually filled forms. This data is then entered and stored into databases in government and private organizations

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from a set of three classifiers for second-stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6 % for Kannada characters on the MILE lab dataset and 90.2 % for Tamil characters on the HP Labs dataset.

This paper focuses on the applicability of the features inspired by the visual ventral stream for handwritten character recognition. A set of scale and translation invariant C2 features are first extracted from all images in the dataset. Three standard classifiers kNN, ANN and SVM are then trained over a training set and then compared over a separate test set. In order to achieve higher recognition rate, a two stage classifier was designed with different preprocessing in the second stage. Experiments performed to validate the method on the well-known MNIST database, standard Farsi digits and characters, exhibit high recognition rates and compete with some of the best existing approaches. Moreover an analysis is conducted to evaluate the robustness of this approach to orientation, scale and translation distortions.

One of the most researched topics with wide implementations in different fields is Handwritten Character Recognition
(HCR). To utilize this technology in automated data-entry applications, this is one of the fields on which past and recent works
have been done to focus on a variety of languages. Individual characters in a word are recognized through a set of images,
which are then studied by the Deep Neural Network. It is with the help of this recognition that results are ranked. This ranking
is performed based on the client’s request. A Convolutional Deep Neural Network Model is considered to acknowledge the
handwritten characters in this paper. It learns, using local receptive areas and heavily connected neural network layers, a useful
set of ordination which are required to generate an optimum result.

We present a framework for handwritten Bangla digit recognition using Sparse Representation Classifier. The classifier assumes that a test sample can be represented as a linear combination of the train samples from its native class. Hence, a test sample can be rep- resented using a dictionary constructed from the train samples. The most sparse linear representation of the test sample in terms of this dictionary can be efficiently computed through l1-minimization, and can be exploited to classify the test sample. We applied Sparse Representation Classifier on the image zone density, an image domain statistical feature extracted from the character image, to classify the Bangla numerals. This is a novel approach for Bangla Optical Character Recognition, and demonstrates an excellent accuracy of 94% on the off-line handwritten Bangla numeral database CMATERdb 3.1.1. This result is promising, and should be investigated further.

This paper proposes a novel method of recognition of hand written character alphabets artificial neural networks together with conventional techniques. However in real situation it is not easy to build robust recognition because of vast variations in personal writing styles. There are also differences in one person's writing style depending on the context, mood of writer and writing situation. The

An on-line handwritten character recognition technique based on a template matching distance is proposed. In this method, the pen-direction features are quantized using the 8-level Freeman chain coding scheme and the dominant points of the stroke are identified using the first difference of the chain code. The distance between two symbols results from the difference of the respective chain codes

Various handwriting styles are unique in this manner, making it challenging to identify characters that were written by hand. Handwritten character recognition has become the subject of exploration over the last few decades through an exploration of neural networks. Languages written from left-to-right, such as Hindi, are read from start-to-finish design. To recognize these types of writing, we present a Deep Learning-based handwritten Hindi character recognition system utilizing deep learning techniques such as Convolutional Neural Networks (CNN) with Optimizer Adaptive Moment Estimation (Adam) and Deep Neural Networks (DNN) in this paper. The suggested system was trained on samples from a large number of database images and then evaluated on images from a user-defined data set, yielding extremely high accuracy rates.

The handwriting styles of the various individuals are unique in this manner; it is truly challenging to recognize the manually written characters. Manually written character acknowledgment is a space of example that has turned into the subject of exploration during the last few decades. The neural organization assumes a significant part in transcribed person acknowledgment. This requires the study and reproduction of Artificial Neural Networks. In Neural Network, every hub plays out a few straightforward calculations and every association passes a sign starting with one hub on then onto the next marked by a number called the "association strength" or weight, demonstrating the degree to which sign is intensified or reduced by the association. Hindi content is written from left to right and from start to finish design.

... Segmentation of Lines and Words in Handwritten Gurmukhi Script Documents Munish Kumar Computer Science Department GGS College for Women Chandigarh, India +919465409800, +919256086312 munishcse@gmail.com ... 117-135. [3] Pal, U., and Chaudhuri, BB 2004. ...