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CN104898855A - System and method for inputting texts on basis of devices with rockers - Google Patents

System and method for inputting texts on basis of devices with rockers Download PDF

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CN104898855A
CN104898855A CN201510305823.XA CN201510305823A CN104898855A CN 104898855 A CN104898855 A CN 104898855A CN 201510305823 A CN201510305823 A CN 201510305823A CN 104898855 A CN104898855 A CN 104898855A
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input
handwriting
word
letter
model
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CN104898855B (en
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顾振宇
徐兴亚
储程
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Shanghai Jiao Tong University
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Shanghai Jiao Tong University
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Abstract

一种基于带摇杆设备的文本输入系统及方法,包括:输入设备、笔迹特征提取模块、笔迹SVG模型、笔迹模型训练模块、词库联想算法模块、界面信息控制模块、显示设备,其中:输入设备与笔迹特征提取模块相连并传递用户输入的笔迹的一系列平面坐标,笔迹特征提取模块和笔迹模型训练模块均与笔迹SVG模型相连并分别输入笔迹特征信息和输入结果反馈信息,笔迹SVG模型分别与词库联想算法模块和界面信息控制模块相连并输出笔迹识别的结果,词库联想算法模块与界面信息控制模块相连并在联想模式下输出筛选出的最有可能的单词,界面信息控制模块与显示设备相连并输出动画信息,用户根据输入结果正确与否将反馈信息传入笔迹模型训练模块,用于训练新的笔迹SVG模型,从而不断提高识别的准确率,本发明能够显著提高输入效率。

A text input system and method based on a device with a rocker, comprising: an input device, a handwriting feature extraction module, a handwriting SVG model, a handwriting model training module, a lexicon association algorithm module, an interface information control module, and a display device, wherein: input The device is connected to the handwriting feature extraction module and transmits a series of plane coordinates of the handwriting input by the user. Both the handwriting feature extraction module and the handwriting model training module are connected to the handwriting SVG model and input the handwriting feature information and input result feedback information respectively. The handwriting SVG model is respectively It is connected with the lexicon association algorithm module and the interface information control module and outputs the result of handwriting recognition, the thesaurus association algorithm module is connected with the interface information control module and outputs the most likely words screened out in association mode, and the interface information control module is connected with the interface information control module. The display device is connected and outputs animation information, and the user sends the feedback information to the handwriting model training module according to whether the input result is correct or not, for training a new handwriting SVG model, thereby continuously improving the accuracy of recognition, and the invention can significantly improve input efficiency.

Description

Based on text input system and the method for band rocking bar equipment
Technical field
What the present invention relates to is a kind of technology of field of computer peripherals, specifically a kind of text input system based on band rocking bar equipment and method.
Background technology
Be sitting in when sofa using Xbox or intelligent television, the letter inputting several similar user name or movie title is conveniently very common thing.Demand game host and intelligent television using rocking bar carry out text event detection exists always, and increases fast along with greatly enriching of internet content.The new technologies such as speech recognition still can not replace traditional inputting interface, as keyboard and rocking bar under many circumstances as a kind of optional text event detection scheme.The text event detection demand such as registration, search, note, Email increased on intelligent television or game host, effective text event detection means will improve an experience for these functions greatly, also lay a good foundation for note and further developing of Email.
Rocking bar kind equipment is widely used in intelligent television, game and vehicle entertainment system as a kind of instantiate live Controls, it comprises rocking bar or its variant form, include but not limited to game paddle rocking bar, handheld device rocking bar, vehicle-mounted Comprehensive Control knob, annular multi-direction button.
The method that use rocking bar main at present carries out text event detection is that cursor selects virtual keyboard input method.The method shows one piece of dummy keyboard on screen, uses rocking bar to move cursor and select the letter on keyboard to input, wherein:, alphabetic keyboard and Qwerty keyboard are most popular keyboard arrangement modes.This character input method is widely used in game machine (PSP as Sony slaps machine and PS main frame, the Xbox main frame etc. of Microsoft), intelligent television, vehicle-mounted information and entertainment system (as BMW iDrive, benz COMAND, Audi MMI etc.).The advantage of this input mode is that study threshold is low, and shortcoming is that efficiency is lower, and input process is dry as dust, and the notice keeping dummy keyboard owing to needing user, this input method can not realize blind input.
There is researcher to propose abroad and be similar to hand-written new input way, such as Graffiti, Unistrokes and EdgeWrite.Rocking bar is difficult to accurately " to write " the complex plane track of letter due to its physical property, and therefore these input methods have carried out simplifying writing to facilitate to letter.Graffiti and Unistrokes is that the use pointer proposed the nineties in 20th century carries out hand-written input method.They, by letter being simplified to a stroke, are easy to write, and improve the effect of machine recognition.The physical edge that EdgeWrite adds a square frame assists rocking bar to write.The font of each letter contains angle on a series of square frame and limit.Compared with selecting inputting method with tradition, these input methods comparatively select inputting method to have can the advantage of blind input.But user needs to learn new alphabet, therefore input speed is at the beginning slow.Game Controller Text entry with Alphabetic and Multi in CHI meeting in 2007 ?Tap Selection Keyboard article propose the input method using two rocking bars on Xbox360 handle to control two keyboards respectively to carry out inputting.When this input method uses input through keyboard based on user, right-hand man is responsible for two zoness of different respectively, qwerty keyboard be divide into two pieces and improves input efficiency.But still need the notice that user keeps dummy keyboard, blind input cannot be realized.
Through finding the retrieval of prior art, open (bulletin) the day 2005.04.20 of Chinese patent literature CN1607491, disclose a kind of Chinese version word input system and method, allow user by using operating rod or its coordinator, add the first few stroke that word needs, word is input in the device of such as mobile phone or PDA.Because just mobile operating bar adds the one or more strokes being used for starting written word, even if or sometimes before the arbitrary stroke of interpolation, user also can find a word wanted from the option table of display.This option table be context-sensitive, rely on last time input word and different so that user can have the word wanted of the candidate of maximum possible.But the prior art is compared with the present invention, its insurmountable technical matters comprises that 31 kinds of stroke forms of Chinese character operate with 5 kinds of operating rod morphologically can not be completely corresponding, and novice users needs memory, and study threshold is higher; The tilted direction of operating rod moves and is difficult to control, easy maloperation when moving to 4:30 direction and 7:30 direction in this patent; Lack the mechanism helping user in use to learn; Feedback cannot being seen on software interface during operation, when carrying out a stroke input as used operating rod, the stroke of concrete input can not be seen on software interface.
Open (bulletin) the day 2005.01.19 of Chinese patent literature CN1567160, disclose a kind of input device and How It Works thereof, input device comprises: a direction device, to detect and to export a plurality of direction, export one of those corresponding directions for during input one word according to character stroke handle bar device for steering; One control circuit is coupled to direction device, one of those directions export direction device coding and storage, and compares with database, and exports the plural candidate word in one of those directions corresponding.This How It Works comprises: receive input one direction; By direction encoding and storage; Compare with a database, export a plurality of candidate characters in this direction corresponding.This technology utilizes the direction device input characters of Joystick-type or steering-wheel type, only need utilization orientation information can be depicted according to stroke by word and reach input characters result once finger steering; Input through keyboard need not be used only to use direction device to coordinate Input Software just can reach text event detection and space can be saved; Only with on the other hand accusing direction processed and input characters need not departure direction device, therefore use simply.But the prior art is compared with the present invention, its insurmountable technical matters comprises between character stroke with direction good not corresponding, is difficult to memory; Some direction output unit such as rocking bar is difficult to accurately export 8 directions, particularly tilted direction; Lack picture feedback; Lack the mode helping user learning to use.
Summary of the invention
The present invention is directed to prior art above shortcomings, propose a kind of text input system based on band rocking bar equipment and method, can conveniently realize blind defeated, the learning difficulty that make use of rocking bar is low, makes the present invention to significantly improve input efficiency.
The present invention is achieved by the following technical solutions:
The present invention relates to a kind of text input system based on band rocking bar equipment, comprising: the association of input equipment, handwriting characteristic extraction module, person's handwriting SVG model, handwriting model training module, dictionary algoritic module, interface information control module, display device, wherein:
Input equipment is connected with handwriting characteristic extraction module and transmits a series of planimetric coordinatess of the person's handwriting that user inputs, handwriting characteristic extraction module and handwriting model training module are all connected with person's handwriting SVG model and input person's handwriting characteristic information and input results feedback information respectively, person's handwriting SVG model is associated algoritic module respectively and is connected with interface information control module and exports the result of person's handwriting identification with dictionary, dictionary association algoritic module is connected with interface information control module and under association's pattern, exports the most possible word filtered out, interface information control module is connected with display device and exports animation information, user imports feedback information into handwriting model training module according to input results correctness, for training new person's handwriting SVG model, thus improve constantly the accuracy rate of identification.
Under association's input pattern, person's handwriting SVG model and dictionary are associated algoritic module and are connected and the result transmitting person's handwriting identification, namely possible letter and probability thereof; Under non-association pattern, person's handwriting SVG model is directly connected with interface information control module, and the result transmitting person's handwriting identification is selected for user, and the quantity according to probability size alternative result is no more than 3.
Described person's handwriting SVG model, carries out learning classification by on-line study mechanism to the handwriting trace of user and correct recognition result, to be optimized SVG model, thus improves the accuracy rate of character classification further.
The present invention relates to the text entry method of said system, comprise the following steps: first by adopt the rocking bar touching in rocking bar motion or leave border, rocking bar gets back to center and reverse three kinds of states split track and according to curvature threshold segmentation characteristic vector pickup track characteristic on border, and be trained to the SVG model of the handwriting trace for Real time identification user, adopt SVG model to carry out character classification judgement to the rocking bar input trajectory carrying out feature extraction equally again, and export a series of possible letter and probability thereof.
Described method preferably switches between association's input pattern (whole word input) and non-association input pattern (letter-by-letter input), under association's input pattern, the character classification judged result of all tracks inputted provides the highest word of probability by calculating joint probability with the word in dictionary, and along with continuation input continuous renewal.Under non-association input pattern, probability is higher than threshold value and the character judged result that quantity is no more than three will be supplied to user selects.
Described method, specifically comprises the following steps:
Step 1, gather initial user letter track sample characteristics train SVG learning model, specifically comprise:
The planimetric coordinates of the alphabetical handwriting trace of 1.1 reading;
1.2 according to rocking bar motion in rocking bar touching or leave border, rocking bar gets back to center and whether reverse three kinds of condition adjudgement add new stroke on border, each stroke is made up of a series of coordinate points;
1.3 screen stroke according to sampled point quantity, remove noise;
Each stroke in 1.4 tracks is split the proper vector for being similar to straight-line segment according to certain curvature threshold again;
1.5, from the multinomial feature of each characteristic vector pickup, comprise distance, angle, absolute position, absolute angle, absolute distance and skew;
1.6 according to track characteristic set training SVG model.
Step 2, a rocking bar input trajectory to be identified, specifically comprises:
2.1 gather a rocking bar input trajectory coordinate data;
2.2 extract track characteristic;
2.3 import SVG learning model;
2.4 export recognition result, comprise multiple letter and probability thereof, when present mode is for association's input pattern, enters step 3, otherwise enter step 4;
Step 3, according to track identification result, dictionary to be screened, specifically comprises:
3.1 remove according to this recognition result the word losing possibility in dictionary;
3.2 judge now whether dictionary is empty;
If 3.3 dictionaries are sky provide prompting, if not for empty removal in input field superposes impossible letter in alphabetical group.
In step 4, selection candidate list, letter inputs, and specifically comprises:
4.1 screen recognition result according to probability threshold values, only retain the letter that probability is greater than threshold values;
Letter the highest for probability shows by 4.2 by default, and in addition first three recognition result of probability is alternatively shown;
If there is no target letter in 4.3 acquiescences or candidate list, delete current results and re-enter, otherwise continue next step;
The selected letter of 4.4 users;
The selected letter of 4.5 input field display input;
SVG model is upgraded according to letter and corresponding input trajectory after 4.6 successful input alphabets.
Step 5, selection word or continuation input alphabet, specifically comprise:
5.1 use HMM algorithms to calculate joint probability according to the word frequency of the possible result of letter each in word and probability and word;
Word in dictionary to arrange by probability and alternatively shows by 5.2 from big to small, and upgrades picture default word and word candidates table;
If 5.4 select word from candidate list, then input this word; If do not selected, continue input alphabet track, repeat step 1 ~ 4, until selected word;
5.4 words input successfully and upgrade SVG model according to letter and corresponding input trajectory.
Technique effect
Compared with prior art, invention increases the efficiency using rocking bar kind equipment to carry out English input.On the one hand, the study threshold for novice users is very low, and input efficiency is higher at the very start.On the other hand, on-line study mechanism continuous renewal user writes sample, and when making to use for a long time, system meets user writing custom more, and input speed and accuracy rate significantly promote.
Accompanying drawing explanation
Fig. 1 is the exemplary function structure chart of input method system;
Fig. 2 is the algorithm flow schematic diagram extracting characteristic module;
Fig. 3 is the algorithm flow schematic diagram of training SVG model;
Fig. 4 is the algorithm flow schematic diagram of association's input pattern;
Fig. 5 is the algorithm flow schematic diagram of non-association input pattern;
Fig. 6 is a kind of schematic diagram that may use the rocking bar equipment Xbox360 handle of this input system;
Fig. 7 is a kind of schematic diagram that may use the rocking bar equipment PSP game machine of this input system;
Fig. 8 is a kind of schematic diagram that may use the rocking bar equipment TV/set-top box remote controller of this input system;
Fig. 9 is a kind of schematic diagram that may use the rocking bar equipment vehicle mounted guidance knob of this input system;
Figure 10 uses rocking bar to write letter and uses touch pad to write the schematic diagram of the track of letter;
Figure 11 is the schematic diagram of the English inputting interface of the present invention.
Embodiment
Elaborate to embodiments of the invention below, the present embodiment is implemented under premised on technical solution of the present invention, give detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment 1
As shown in Figure 6, a kind of rocking bar kind equipment Xbox360 handle of this implementation system support.Xbox360 handle has two rocking bars, user can use one of them rocking bar to write, and uses another rocking bar to select.In user test, employ the rocking bar in the upper left corner for writing, the rocking bar in the lower right corner is selected, and actually can set according to user habit.Three buttons X, A, B on handle are set to delete, determine and associate mode switch function respectively.Figure 11 example inputting interface comprises input field and association's candidate bar, and input field is for showing feedback animation and the letter after determining, association's candidate bar for selecting possible word, does not exist in association's pattern in non-association pattern.
The recognition method using rocking bar to carry out writing has used for reference touch pad to a certain extent.But both still have huge difference.As shown in Figure 10, the track on touch pad spreads out in one plane and has obvious breakpoint, and the track using rocking bar to write normally continuous print, a lot of stroke can overlap on border.This is because when using rocking bar to write, moving near physical boundary is the most natural and full blast.In order to split stroke, after track is divided into several stroke, more respectively according to curvature threshold values, cutting is carried out to each stroke part.Therefore each track can be subdivided into multiple stroke, and each stroke is made up of a series of near linear section again.For each near linear section, be all therefrom extracted 7 features, comprise distance, angle, absolute position, absolute angle, absolute distance and skew.Certainly because a letter may have multiple literary style, therefore each letter has the track of multiple correspondence.
Write the feature that extracts sample for being trained to a SVG (Scalable Vector Graphics) model from user, this model will be used for the handwriting trace of Real time identification user.For sample supplier, this system is more accurately at the very start.For other users, find that the accuracy of diversity on system of sample has significant impact.Except allowing sample as far as possible cover more how possible writing style, the on-line study mechanism of proposition can improve the recognition effect of system gradually along with the input of user.Two kinds of input patterns are provided: association's input pattern and non-association input pattern in system.
In association's input pattern, user inputs by word.For each alphabetical track of user writing, SVG model all can provide multiple possible recognition result and corresponding probability.In the letter identified, accuracy rate is excluded lower than the meeting of threshold values, and the letter that remaining accuracy rate is higher is presented on input field cursor place in the mode of superposition after the screening of HMM algorithm.In the input process of a word, user often inputs a letter, the word existed in the recognition result of a series of tracks that HMM algorithm all can input in whole word according to user and dictionary calculates joint probability, probability be 0 word result can be excluded, being reflected on picture is exactly corresponding letter disappearance in alphabetical group that superposes, and all the other possible words are arranged in association's hurdle candidate bar from big to small by probability.Therefore the whole input process of a word is exactly the result just having started at every turn to input is all alphabetical group that is superimposed, and along with input alphabetical one by one, the letter of superposition reduces gradually, and the target word of user is appeared in one's mind out gradually.Target word to appear speed in one's mind relevant with the size of dictionary to the character of word itself.When a word inputs successfully, on-line study mechanism will each letter in learning word and corresponding track, makes SVG model more meet the writing style of user, improves recognition accuracy.
In non-association pattern, user often inputs a track, and 3 letters that in the result identified, probability is the highest can be arranged in input cursor awaits user by probability size order and select, and after user selects certain letter, corresponding letter just completes input.This letter and corresponding track will be brought in SVG model through on-line study mechanism, improve the recognition accuracy for this user.

Claims (6)

1.一种基于带摇杆设备的文本输入系统,其特征在于,包括:输入设备、笔迹特征提取模块、笔迹SVG模型、笔迹模型训练模块、词库联想算法模块、界面信息控制模块、显示设备,其中:输入设备与笔迹特征提取模块相连并传递用户输入的笔迹的一系列平面坐标,笔迹特征提取模块和笔迹模型训练模块均与笔迹SVG模型相连并分别输入笔迹特征信息和输入结果反馈信息,笔迹SVG模型分别与词库联想算法模块和界面信息控制模块相连并输出笔迹识别的结果,词库联想算法模块与界面信息控制模块相连并在联想模式下输出筛选出的最有可能的单词,界面信息控制模块与显示设备相连并输出动画信息,用户根据输入结果正确与否将反馈信息传入笔迹模型训练模块,用于训练新的笔迹SVG模型,从而不断提高识别的准确率。1. A text input system based on a rocking bar device, comprising: input device, handwriting feature extraction module, handwriting SVG model, handwriting model training module, lexicon association algorithm module, interface information control module, display device , wherein: the input device is connected to the handwriting feature extraction module and transmits a series of plane coordinates of the handwriting input by the user, the handwriting feature extraction module and the handwriting model training module are both connected to the handwriting SVG model and input handwriting feature information and input result feedback information respectively, The handwriting SVG model is respectively connected with the lexicon association algorithm module and the interface information control module and outputs the result of handwriting recognition. The thesaurus association algorithm module is connected with the interface information control module and outputs the most likely words screened out in association mode. The information control module is connected to the display device and outputs animation information, and the user sends the feedback information to the handwriting model training module according to whether the input result is correct or not, which is used to train a new handwriting SVG model, thereby continuously improving the accuracy of recognition. 2.根据权利要求1所述的系统,其特征是,在联想输入模式下,笔迹SVG模型与词库联想算法模块相连并传递笔迹识别的结果,即可能的字母及其概率;在非联想模式下,笔迹SVG模型直接与界面信息控制模块相连,并传递笔迹识别的结果供用户选择,根据概率大小可选结果的数量不超过3个。2. system according to claim 1, it is characterized in that, under associative input mode, handwriting SVG model links to each other with thesaurus associative algorithm module and transmits the result of handwriting recognition, promptly possible letter and probability thereof; In non-associative mode Next, the handwriting SVG model is directly connected to the interface information control module, and handwriting recognition results are delivered for the user to choose, and the number of optional results does not exceed 3 according to the probability. 3.根据权利要求1所述的系统,其特征是,所述的笔迹SVG模型,通过在线学习机制对用户的书写轨迹和正确的识别结果进行学习分类,以对SVG模型进行优化,从而进一步提高字符分类的准确率。3. The system according to claim 1, wherein the handwriting SVG model learns and classifies the user's writing track and correct recognition results through an online learning mechanism, so as to optimize the SVG model, thereby further improving Accuracy of character classification. 4.一种根据上述任一权利要求所述系统的文本输入方法,其特征在于,包括以下步骤:首先通过采用摇杆运动中的摇杆触碰或离开边界、摇杆回到中心和在边界上反向三种状态来分割轨迹并根据曲率阈值分割特征向量提取轨迹特征,并训练成用于实时识别用户的书写轨迹的SVG模型,再采用SVG模型对同样进行特征提取的摇杆输入轨迹进行字符分类判断,并输出一系列可能的字母及其概率。4. A text input method according to the system according to any one of the preceding claims, characterized in that it comprises the following steps: first, by using the rocker in the rocker movement to touch or leave the boundary, the rocker returns to the center and in the boundary Segment the trajectory with the three states up and down, and extract the trajectory features according to the curvature threshold segmentation feature vector, and train it into an SVG model for real-time recognition of the user's writing trajectory, and then use the SVG model to perform feature extraction on the joystick input trajectory. Character classification judgment, and output a series of possible letters and their probabilities. 5.根据权利要求4所述的方法,其特征是,在联想输入模式(整词输入)和非联想输入模式(逐字母输入)间切换,在联想输入模式下,已经输入的所有轨迹的字符分类判断结果将与词库中的单词计算联合概率给出概率最高的单词,并随着继续输入不断更新。在非联想输入模式下,概率高于阈值且数量不超过三个的字符判断结果将提供给用户进行选择。5. The method according to claim 4, characterized in that, switching between associative input mode (whole word input) and non-associative input mode (letter-by-letter input), in the associative input mode, the characters of all tracks that have been imported The result of the classification judgment will be combined with the words in the thesaurus to calculate the joint probability to give the word with the highest probability, and it will be updated continuously as the input continues. In the non-associative input mode, judging results of characters whose probability is higher than the threshold and whose number does not exceed three will be provided to the user for selection. 6.根据权利要求4或5所述的方法,其特征是,所述的方法,具体包括以下步骤:6. according to the described method of claim 4 or 5, it is characterized in that, described method specifically comprises the following steps: 步骤1、采集初始用户字母轨迹样本特征并训练SVG学习模型,具体包括:Step 1. Collect the characteristics of initial user letter trajectory samples and train the SVG learning model, including: 1.1读取字母书写轨迹的平面坐标;1.1 Read the plane coordinates of the writing track of letters; 1.2根据摇杆运动中的摇杆触碰或离开边界、摇杆回到中心和在边界上反向三种状态判断是否添加新笔划,每个笔划都由一系列坐标点构成;1.2 Judging whether to add a new stroke according to the three states of the joystick touching or leaving the boundary, the joystick returning to the center and reversing on the boundary during the movement of the joystick, each stroke is composed of a series of coordinate points; 1.3根据采样点数量对笔划进行筛选,去除噪音;1.3 Screen strokes according to the number of sampling points to remove noise; 1.4轨迹中的每个笔划再根据一定的曲率阈值被切分为近似于直线段的特征向量;1.4 Each stroke in the trajectory is divided into feature vectors similar to straight line segments according to a certain curvature threshold; 1.5从每个特征向量提取多项特征,包括距离、角度、绝对位置、绝对角度、绝对距离和偏移;1.5 Extract multiple features from each feature vector, including distance, angle, absolute position, absolute angle, absolute distance, and offset; 1.6根据轨迹特征集合训练SVG模型;1.6 Train the SVG model according to the trajectory feature set; 步骤2、对一次摇杆输入轨迹进行识别,具体包括:Step 2. Identify the trajectory of a joystick input, including: 2.1采集一次摇杆输入轨迹坐标数据;2.1 Collect the coordinate data of the joystick input trajectory once; 2.2提取轨迹特征;2.2 Extract trajectory features; 2.3导入SVG学习模型;2.3 Import SVG learning model; 2.4输出识别结果,包含多个字母及其概率,当当前模式为联想输入模式时,进入步骤3,否则进入步骤4;2.4 Output the recognition result, including multiple letters and their probabilities. When the current mode is the associative input mode, go to step 3, otherwise go to step 4; 步骤3、根据轨迹识别结果对词库进行筛选,具体包括:Step 3. Filter the thesaurus according to the trajectory recognition results, specifically including: 3.1根据此次识别结果去除词库中失去可能性的单词;3.1 According to the recognition result, remove the words that are out of possibility in the thesaurus; 3.2判断此时词库是否为空;3.2 Determine whether the thesaurus is empty at this time; 3.3如果词库为空给出提示,如果不为空去除输入栏中叠加字母组中不可能的字母;3.3 If the thesaurus is empty, give a prompt, if it is not empty, remove the impossible letters in the superimposed letter group in the input field; 步骤4、选择候选表中字母进行输入,具体包括:Step 4. Select the letters in the candidate list for input, including: 4.1根据概率阀值对识别结果进行筛选,只保留概率大于阀值的字母;4.1 Screen the recognition results according to the probability threshold, and only keep the letters whose probability is greater than the threshold; 4.2将概率最高的字母作为默认显示,除此之外概率前三识别结果作为候选表;4.2 The letter with the highest probability is displayed as the default, and the top three recognition results are used as the candidate list; 4.3如果默认或候选表中没有目标字母,删除当前结果重新输入,否则继续下一步;4.3 If there is no target letter in the default or candidate list, delete the current result and re-enter, otherwise continue to the next step; 4.4用户选定字母;4.4 User selected letters; 4.5输入栏显示输入选定字母;4.5 The input column displays the selected letter; 4.6成功输入字母后根据字母和相应输入轨迹更新SVG模型;4.6 After successfully inputting letters, update the SVG model according to the letters and corresponding input tracks; 步骤5、选择单词或继续输入字母,具体包括:Step 5. Select a word or continue to input letters, including: 5.1使用HMM算法根据单词中每个字母可能的结果及概率和单词的词频计算联合概率;5.1 Use the HMM algorithm to calculate the joint probability based on the possible results and probabilities of each letter in the word and the word frequency of the word; 5.2将词库中单词按概率从大到小排列作为候选表,并对画面默认单词和单词候选表进行更新;5.2 Arrange the words in the thesaurus in descending order of probability as a candidate list, and update the default word and word candidate list on the screen; 5.4如果从候选表中选定单词,则输入该单词;如果不做选择,继续输入字母轨迹,重复步骤1~4,直至选定单词;5.4 If you select a word from the candidate list, enter the word; if you do not make a choice, continue to enter the letter track, repeat steps 1 to 4, until the word is selected; 5.4单词输入成功后根据字母和相应输入轨迹更新SVG模型。5.4 After the word is successfully entered, the SVG model is updated according to the letter and the corresponding input trajectory.
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