An Improved Pre-classification Method for Offline Handwritten Chinese Character Using Four Corner Feature

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1 ISBN (Print), (CD-ROM) Proceedings of the International Symposium on Intelligent Information Systems and Applications (IISA 09) Qingdao, P. R. China, Oct , 2009, pp An Improved Pre-classification Method for Offline Handwritten Chinese Character Using Four Corner Feature Xin Zhang 1, 2, XueDong Tian 1, 2, and Fang Yang 1, 2 1 College of Mathematics and Computer Science, Hebei University, Baoding Hebei, China zhangxin5313@yahoo.com.cn 2 Hebei key laboratory of Machine Learning and Computational Intelligence, Hehei University,Baoding Hebei, China zhangxin5313@yahoo.com.cn Abstract Pre-classification can effectively improve the performance of handwritten Chinese character recognition. This paper presents a method that uses four corner feature for pre-classification of handwritten Chinese characters. Considering writing variations, we define a set of basic stroke structures and match them with the structures in four corner regions of character image. The matching result will be four feature codes that can be used for character pre-classification. The experiment on 500 Chinese characters from GB2312 shows that our approach can achieve a satisfied result in pre-classification for handwritten Chinese characters. Index Terms Handwritten Chinese character recognition, Pre-classification, Structural feature I. INTRODUCTION Chinese characters amount to as huge as about 10 million words or so. The number of commonly used characters specified in GB2312 is For such big scale of characters, direct recognizing will greatly reduce the recognition speed. Narrow the scope of Chinese characters to be recognized and reduce the complexity of recognition by adding multi-level pre-classification is an effective way to improve system efficiency. Ref. [1] presents the sub-stroke center model in pre-classification, extracting four typical strokes of Chinese characters, the coordinate of stroke center and the number of every same kind of stroke are used as the features of preclassification. Ref. [2] defines a set of structures that are composed of typical strokes and relationships among strokes, and these structures are used to classify characters. It proves that structural feature is feasible and effective in pre-classification of handwritten Chinese character. Because of the complex structures of Chinese characters, different writing habits or interference in the writing process will lead to deformation of the characters and to form different writing styles, which makes the ways of extraction of overall structural features more complicated in the off-line handwritten Chinese character recognition. The outer structure of Chinese characters not only contains abundant information, but also can be kept relatively stable and integrate even when the overall structure of the Chinese character is changed a lot. Ref. [3] mentioned a method combining structural and statistical 2009 ACADEMY PUBLISHER AP-PROC-CS-09CN features for recognition. Fuzzy set theory is used in features extraction while SEART neural network model is used as a classifier to minish the interference from writing variations of Chinese characters. Ref. [4] proposed a simple pre-classification scheme based on four corner coding: stroke features in four corner regions are extracted in order to create several groups of feature code and the codes are the basis of pre-classification. In case of mis-preclassification, a feedback mechanism will generate a new set of feature codes to adapt writing variations. On the basis of Ref. [4], this paper improves the selection and extraction of feature primitive and presents a pre-classification method for off-line handwritten Chinese character using four corner features. Select several simple structures that are not susceptible to writing variations as feature primitives and each primitive corresponds to a decimal value. Extract the properties of the stroke within four corner regions of character and match the properties with feature primitives. The matching result is a 4-D feature code, and that is the basis of pre-classification. II. FOUR CORNER FEATURE Ref. [4] defines 9 typical stroke primitives and matches them with the strokes within specific regions of Chinese character, using the matching result as the feature of preclassification. These stroke primitives include simple stroke primitives and composite stroke primitives, which are very similar with traditional four corner code. This paper continue the idea of Ref. [4]: taking the feature primitives of the traditional four corner code as a basis, considering the handwritten Chinese character structures and image processing features, so as to further improve the stroke primitives in pre-classification. Extract the stroke primitives mentioned in Ref. [4] and traditional four corner code, problems shows as follow: Figure 1. Length divergence of simple stroke primitive.

2 Figure 2. Separate composite stroke primitive. 1) The difference among simple strokes is susceptible to writing variations, and that would result in misrecognition among the primitives. As shown in Fig. 1, the length divergence between the first strokes on the left of two characters is too large that traditional four corner code method can not distinguish it between left falling stroke and dot. 2) The components in composite strokes are independent with each other and also difficult to be recognized as a unity. When the first component is found, the left components are always lost or found by error. In Fig. 2, the composite stroke on top of the character is one of composite primitive as a whole in traditional four corner code, but the three components are not only separate but also far away from each other. That makes the extraction and recognition fallible. Through the observation and study of a large number of handwritten Chinese character images, it can be found that the kinds of writing variations can not be counted out, but there are three stroke structural features that are much more stable than others: inflexion, crossing and direction. On the basis of primary Chinese character stroke, traditional four corner code and stroke primitive in Ref.[4], abandoning composite stroke primitive and the discrimination of stroke length, taking inflexion, crossing and direction as key basis for criteria, there are 7 stroke primitives defined that are typical and easy to extract, as shown in Table I. Chinese character strokes consist of stroke dots. Define stroke dots in four kinds: 1) Terminal: starting point and end point of the stroke; 2) Intersection point: including forkpoint and crosspoint; 3) Inflexion point; 4) Transition point: the connect point among the three stroke points above. Steps of feature extraction algorithm are shown as follows: 1) Scan the character image by 225,315,135,45 from four corners to the center of the image as shown in Fig. 6. Stop scanning when the first black pixel is found, and the stroke which the black pixel belongs to is the feature stroke of the character in this corner. Figure 3. Character image after pre-processing. Figure 4. Example of crosspoint. TABLE I. NEW FEATURE PRIMITIVES Code Feature primitive Description Figure 5. Example of forkpoint. 0 Horizontal 1 Vertical 2 Left-falling 3 Right-falling 4 Crossing 5 Two crossings Figure 6. Determination of feature stroke. 6 Inflexion III. FOUR CORNER CODE EXTRACTION A. Extraction algorithm of four corner feature Thresholding and thinning should be done firstly as pre-processing to character images before feature extraction. Figure 7. 8-neighborhood template. 395

3 2) Track and record feature stroke with 8-neighborhood template in Fig. 7. When the first black pixel is found and feature stroke is determined, the search of the terminal should begin from the black pixel. P in template is active pixel point, P[0]-P[7] stand for pixel points in correspondence positions of the template, sum is the total number of the black pixel from P[0] to P[7]. Tracking rules are as follows: Sum=1, P is the terminal of feature stroke. If P is the first terminal found, the recording of the stroke dots track begins. If P is the second terminal, it means that the feature stroke is completely found. Sum=2, P is a transitional point of feature stroke. Record this pixel and move the center of template to the other black pixels that have not been recorded. Sum=3, P is a forkpoint of the feature stroke. Choose the next proper point and move the center of template to it. Sum=4, P is a crosspoint of feature stroke. Add 1 to the total number of the crosspoints, and choose the next proper point, moving the center of template to it. 3) When the first terminal of feature stroke is found, repeat step 2 and begin to record stroke tracking. 4) If both of the two terminals of feature stroke are found, the tracking and recording of feature stroke should be finished and the analysis of feature stroke properties begins. In this algorithm, the judgment of the next point in forkpoint and crosspoint determines whether we can correctly capture the strokes orientation. It plays a crucial role in feature stroke extraction. There are four branches for every crosspoint, but only two of them belong to feature stroke. The research on the structure of Chinese characters finds that the angle between two branches that belong to the same feature stroke is probably 180. Steps of the judgment of the next point in crosspoint are shown as follows: 1) Set P as the crosspoint, P 0,P 1,P 2 and P 3 are new points that are all 5 points away from P in each of the four branches (as shown in Fig. 8). P 0 is in the branch that has been recorded. 2) D D D 2DD x 0 x0 P0PPx arccos( ), x 1,2,3 (1) 0 x Where D0 Distance( P, P0), DX Distance( P, PX), D0X Distance( P0, PX). 3) Compare the three angles above and choose the one most proximal to 180 for the direction of next point. Record this point and add 1 to the total number of the crosspoint. Figure 8. The judgement of the next point in forkpoint and crosspoint. Forkpoint exists mainly in the area that two strokes osculate but not intersect (as shown in Fig. 5).One of the two strokes is feature stroke while the other one is useless. There are two situations about forkpoint in our algorithm: First, the forkpoint is a terminal of the feature stroke, and the other two branches belong to another stroke; Second, the forkpoint is a transitional point of the feature stroke, two of the three branches constitute feature stroke. As the character in Fig. 5, when the feature stroke is rightfalling in bottom right corner, the forkpoint is a terminal of feature stroke; when the feature stroke is lefttfalling in bottom left corner, the forkpoint is a transitional point. The judgment of the next point in fork-point is similar with the judgment in crosspoint. While besides P 0 PP 1 and P 0 PP 2, P 1 PP 2 should be taken into account, too, as shown in Fig. 8. Compare the three angles with 180, if P 0 PP 1 or P 0 PP 2 is the nearest one, the correspondence branch will be the direction of next point. But if P 1 PP 2 is the nearest one to 180, it means that the fokepoint is a terminal of feature stroke. The fokepoint should be recorded and the tracking of feature stroke should be terminated. By the feature stroke extraction described above, we can get a point coordinate series of feature stroke and the total number of the crosspoints. The last basis for the matching of feature primitive is inflexion. Because the point coordinate of inflexion is not needed, the existence of inflexion will be measured simply in the process of feature primitive matching. B. Feature primitive matching The matching of feature primitive matching is based on three bases: inflexion, crossing and direction. The total number of crossing is known by feature stroke extraction, inflexion and direction will be as follows: Set the first and the last point of feature stroke as A and B. Distance( AB, ) a (2) LS where L s is the length of the feature stroke. If a is less than a threshold, this stroke is considered to have inflexion. The threshold is given by experience. The direction of the feature stroke is judged by angle. Set the first and the last point from the point coordinate series of feature stroke as A(i,j) and B(i,j). a is the angle of the feature stroke. a arccos( Bj Aj ) 2 2 ( Bj Aj) ( Bi Ai) Thus the property set that consists of inflexion, crossing and direction is completed. Match the feature strokes with stroke primitives by the property set. The process of matching is described below: switch(total number of the crossings) { case 0: if(inflexion) feature code=6; (3) 396

4 } else Judge by angle threshold feature code =0,1,2,3; case 1: feature code =4; default : feature code =5; Stroke primitives The angle threshold TABLE II. THE ANGLE THRESHOLD OF STROKE PRIMITIVES IV. EXPERIMENT A. The Process of the Experiment The experiment is carried out by choosing 500 handwritten Chinese characters from GB2312 randomly. Extract the feature stroke from four corner region of the Chinese characters by using the extraction algorithm, and match them with stroke primitives described in Section II. The threshold of inflexion is 0.85, the angle threshold of four simple stroke primitives are shown in Table II. To every Chinese character, we have a 4-D feature code that represents the matching result of the feature primitive described in Section II, and the feature stroke within four corners regions of Chinese character: top left corner, bottom left corner, top right corner and bottom right corner. The four feature strokes of the character in Fig. 9 are dot, dot, intersection of horizontal and leftfalling, and right-falling, they are matched with the feature primitives of right-falling, left-falling, intersection and right-falling, so the feature code of this character is Based on the feature code, this character can be classified in the class with characters that have the same feature code as shown in Fig. 9. B. Experimental results Experiments were performed on 500 handwritten Chinese characters from GB2312, the result of preclassification is shown in Fig. 10. It can be seen from Fig. 10 that the characters are evenly distributed into every class after pre-classification, which proves that the selection of feature primitives is sensible. Though the characters in one class have the same structure in all of the four corners, the similarity of whole structure is very low, and the internal structures also have big divergence (as shown in Table III), that makes the characters in one class can be distinguished accurately by statistical feature and structural feature. On the other hand, as a result of writing variations, every Chinese character will not correspond to one feature code, in another words, a character may not only exist in one class. In Fig. 11, the left top corner region of character shows two different structural relationships in two samples, which will affect the result of pre-classification. Through the experiment of a large numbers of handwritten Chinese character images, we can found that the changes of structural relationship generally exist, but the types of changes are very limited. So the changes of structural relationships break the uniqueness of classification, but it can fit writing variations better and improve the accuracy of pre-classification. Figure 9. Characters in one class. Figure 10. The result of pre-classification. Figure 11. Structural difference in top left corner. TABLE III. EXAMPLE OF PRE-CLASSIFICATION Feature code Characters in the class 1123 示, 栗, 不 2444 升, 华 2626 勿, 用, 豹 V. CONCLUSION In this paper, we propose a improved pre-classification method. Feature primitives are selected by the rules of being steady and easy-extracted. A simple algorithm is designed to extract the feature stroke in four corner regions of the character and to match it with the selected feature primitives, in order to get a 4-D feature code as the basis of pre-classification. The experiment proves that the selection of feature primitives is sensible. Feature extraction algorithm is simple and the experiment gets good results in pre-classification. That one character corresponds to multi-code also promotes the reorganization flexibility of handwritten Chinese characters inflection. But, because of the absence of feedback mechanism, if there are mistakes in the result of pre-classification, the mistakes can not be corrected in 397

5 process of recognition, which will lead to recognition errors. This will be the focus of our research in the future. ACKNOWLEDGMENT This work is supported by National Natural Science Foundation of China under Grant No The Key Scientific Research Project of Hebei Education Department of China under Grant No. ZH REFERENCES [1] Xia-Bi Liu and Yun-De Jia, Chinese Character Structure Models for Handwritten Chinese Character Recognition, Journal of Beijing Institute of Technology, vol. 23, no.3, pp November [2] Rei-Heng Cheng, Chi-Wei Lee, and Zen Chen, Preclassification of handwritten Chinese character based on basic stroke substructures, Pattern Recognition Letters, pp , [3] Hahn-Ming Lee and Chug-Chieh Sheu. A Handwritten Chinese Characters Recognition Method Based on Primitive and Fuzzy Features via SEART Neural Net Model.Systems, Man and Cybernetics, Intelligent Systems for the 21st Century., IEEE International Conference on, Canada, vol.2, pp , October [4] Yiu-Man Tham and Tong Lee. Four Corner Code Based Pre-classification Scheme For Chinese Character Recognition, International Symposium on Speech, Image Processing and Neural Networks, Hong Kong, vol.1, pp , April [5] Jian-Ping Zhao and Dan Che. Stroke Extraction Algorithm for Handwriting Chinese Characters, Journal of Changchun University of Science and Technology, vol.28, no.4, pp.66-70, [6] You-Bin Chen, Xiao-Qing Ding, and You-Shou Wu. A New Feature Extraction Method for Off-line Handwritten Chinese Character Recognition, Signal Processing, vol.14, no.2, pp , [7] Xin-Qiao Lu et al., A Segment Extraction Algorithm Based on Polygonal Approximation for On-Line Chinese Character Recognition, Frontier of Computer Science and Technology, FCST, 2008 Japan-China Joint Workshop on, Japan, pp ,