MULTI-FEATURE EXTRACTION FOR PRINTED THAI CHARACTER RECOGNITION

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1 The Fourth Symposium on Natural Language Processing 2000 MULTI-FEATURE EXTRACTION FOR PRINTED THAI CHARACTER RECOGNITION A. KAWTRAKUL and P. WAEWSAWANGWONG Natural Language Processing and Intelligent Information System Technology Research Laboratory (NAiST), Department of Computer Engineering, Kasetsart University, Thailand This paper presents a simplified printed Thai character recognition system using multiple feature extraction and character classification. Three relevant information extracted from a set of training character images are the direction of each character s contour, the density of character body and character peripheral information. This set of features is used as reference for classifying unknown input characters. Based on Euclidean space model, the category of the reference vector yielding the minimum distance is assigned to the input character pattern. From the experiments, the recognizing speed is 5 character images per second with 97.44% correctness. The performance of recognition can be improved gradually in strengthening the robustness and lowering the error recognition rate by simply training and maintaining the knowledge by users. Key words : character recognition, feature extraction. INTRODUCTION Research interest both in machine printed Thai character and handwritten recognition has been intense in the past decades. Various recognition techniques were employed such as recognition by comparing the stroke changing sequence information (Choruengwiwat 998), recognition based on the statistical approach (Ding 997), recognition based on neural network (Kijsirikul 998; Lursinsap 995; Phokharatkul 998; Tanprasert 995), recognition by comparing the Hough transform information (Phokharatkul 997), recognition based on the fuzzy similarity relation (Taewit 997), recognition based on the expert system (Thumwarin 998). When developing an OCR system, not only classifier or recognizer but several issues need to be considered, i.e., speed, accuracy, image quality and post processing. Recently, there are some Thai OCR software products in the market, but the accuracy of recognizing the Thai characters is still not as high as that of English OCR software products which indicate that their accuracy is 99.9% or that of the Chinese OCR software product (Ding 997) which has the accuracy of 98.6%. The accuracy of English OCR is comparatively higher than that of Chinese OCR since the English character set has much less characters than those in the Chinese character set. In case of Thai OCR system, the accuracy is also not high. The difficulty in Thai OCR (Cooper 995) is that the Thai alphabet contains many pairs of letters that closely resemble each other, for example 'ข ' is almost the same as 'ฃ ' where only the little notch is different. Additionally, Thai character images are not obviously separated in block as the Chinese characters in the Chinese documents, and there are 4 levels of the character position (middle, upper, lower, above upper). These are the sources of ambiguities of segmentation before classifying. This paper presents a simplified and applicable system development for machine printed Thai character recognition. At the current stage, the performance of the system is improved by the assistance of the end users. A design of the system will be described in 2000 The Fourth Symposium on Natural Language Processing 2000

2 2 SNLP 2000, CHIANG MAI, THAILAND section. Section 2 gives a brief of the main part of the current system. The experimental result will be shown in section 3.. A DESIGN OF THAI SEMI-AUTOMATIC OCR SYSTEM A recognition system is usually composed of three major components : Document Analyzer and Preprocessor for document image alignment, locating text blocks and further segmenting into text lines and characters. This component also includes binarization, and normalization. Character Image Recognizer for classifying the input character pattern and outputting the results into an application. Postprocessor for improving accuracy by lexical processing. However, recognizer and its accuracy is the most important part of the system. Additionally, in practical system, more attention should be paid to the speed and knowledge maintenance of the system. Our system is, then, designed as the semi-automatic OCR system. The basic requirements for using the system are the pre-locating the text blocks by the end user and the image quality should not be too poor. The advantage of this system is that the performance can be improved gradually in strengthening the robustness and lowering the error recognition rate with simply training by the end users. Based on the idea that most documents, usually, are typed with only one font, only that font in training set will be selected as the system knowledge base. Accordingly, the speed and accuracy of recognizing will be enhanced. At the current stage, the recognizing speed is 5 character images per second with 97.44% correctness. 2. THE MAIN PART OF THE SYSTEM The main parts of the current system are multiple feature extraction and character classification. Since our approach based on extracted features, the normalization does affect the recognition result greatly. 2. NORMALIZATION There are two problems needed to be processes before recognizing, i.e., how to standardize the size of each image and how to put the image at the center of the frame. The following steps are, then, designed for image s size and position normalization :.) Start the image frame with xsize0 * ysize0 pixels which fit the isolated character. 2.) Rescale the size of the image to xsize * ysize pixels which is the maximum size according to xsize0 or ysize0, i.e., xsize = max(xsize0, ysize0) ysize = max(xsize0, ysize0)

3 MULTI-FEATURE EXTRACTION FOR PRINTED THAI CHARACTER RECOGNITION 3 3.) Find the center of gravity coordinate, (Xcg, Ycg), which is given by X xsize ( i ysize i= j= cg = xsize ysize i= j= pt( i, j)) pt( i, j) Y cg ysize ( j j= i= = xsize ysize xsize i= j= pt( i, j)) pt( i, j) where pt(i, j) is the point in the image at the coordinate (i, j), which pt(i, j) = if it is the black pixel, 0 if white pixel. 4.) Put the character image at the center position of the frame by using the following equation : where CentralizedPt(i, j) = pt( i + Xcg - Xmid, j + Ycg - Ymid ) Xmid = xsize / 2 Ymid = ysize / 2 5) Finally, rescale the image to the size of 36 * 36 pixels as the normalize size. 2.2 MULTI FEATURE EXTRACTION Three relevant information are extracted from a set of training character images : the direction of each character s contour, the density of character s body and the character peripheral information Extracting the Direction of the Character s Contour To extract the direction of the character s contour, the normalized image (36*36 pixels) is divided into 6 * 6 cells. The size of each cell is 6 * 6 pixels. (see Fig.)

4 4 SNLP 2000, CHIANG MAI, THAILAND Figure Dividing the normalized image into 6 * 6 cells for extracting the character contour direction. There are 4 feature windows (see Fig.2) in 3 * 3 pixels, consisting of 4 directions in horizontal (A), vertical (B), left diagonal (C), and right diagonal (D) Figure 2 The 4 Feature Windows Each feature window will be slided through each cell of the normalized image row by row. The contour direction feature vectors, X C, in four directions is, then, represented by the number of matched pixels in all possible location of these 4 windows inside each cell. X C = ( x A, x B, x C, x D, x 2A, x 2B, x 2C, x 2D,..., x 36A, x 36B, x 36C, x 36D ) T Computing the Density of the Character s Body Each normalized character image (36*36 pixels) will be divided into 9 * 9 cells. The size of each cell is 4 * 4 pixels. (see Fig. 3) Figure 3 Dividing the normalized image for extracting the density feature vector into 9 * 9 cells The density feature vector, X D, is the representation of the number of the black pixels in 8 cells, i.e., X D = ( x, x 2, x 3,..., x 8 ) T Extracting the Peripheral Information The peripheral information is the measurement of the distance from the normalized image frame to the nearest character contour within that image. (see Fig. 4) Since there are 4 sides, each side has 36 pixels long, the peripheral feature vector, X P, is : X P = ( x, x 2, x 3,..., x 44 ) T

5 MULTI-FEATURE EXTRACTION FOR PRINTED THAI CHARACTER RECOGNITION 5 Figure 4 The Peripheral Information of the character pattern 2.3 CHARACTER CLASSIFICATION The recognizer of the system is implemented by using the minimum distance classification method (Ding 997). Based on Euclidean space model, the minimum distance will decide the category of the unknown pattern The Reference Feature Vector from the Learning Process To compute the reference feature vector, training set must be collected. At present, 9 normalized characters in 3 fonts are trained as the primary knowledge of the system. The three feature vectors of each character in the same-font sample S will be extracted. Let M be the reference vector of each feature (3 features). M = Avg(X i ), i =..n where X i is the feature vector of each character in S Minimum Distance Classifier At this step, in order to recognize the character, a distance between unknown or input feature vector X and the reference vectors, M k, of category ω k in the training set must be computed. The distance will be computed based on Euclidean model as: d( X, M N k 2 k ) = ( x i m i ) i= where M k = (m k, m k 2,..., m k N ) T, N = 44, 8 and 44 for feature : the direction of each character s contour, feature 2: the density of character body and feature 3: the character peripheral information respectively. To determine the final solution of each input image character, the following steps are needed :

6 6 SNLP 2000, CHIANG MAI, THAILAND.) Compute three sets of the distance between the unknown character and the references based on the three features. The results are as the follows. fset = { d, d 2,..., d P } f2set = { d 2, d 2 2,..., d 2 P } f3set = { d 3, d 3 2,..., d 3 P } where P is the number of the characters in the knowledge base. { d i, d i 2,..., d i P } is the set of distance between the unknown character and the reference based on feature i, i = to 3. 2.) Normalize the three sets mentioned in.) as fset' = { a, a 2,..., a P } f2set' = { a 2, a 2 2,..., a 2 P } f3set' = { a 3, a 3 2,..., a 3 P } where a i j is the normalized value of the distance between the unknown character and the j th reference on feature i. and a i j= d i j / (d i + d i 2 +,...,+ d i P) 3.) Compute the final set of the distance by using the following weight equation : r j = w.a j + w 2.a 2 j + w 3.a 3 j where w, w 2, w 3 are the weight constants based on the statistical information and w + w 2 + w 3 = then the final set is finalset = { r, r 2,..., r P } 4.) The minimum distance, r k, in the final set in 3.) will be selected, where r k r i for all i k Then, the final solution for X is given by ω(x) = ω k

7 MULTI-FEATURE EXTRACTION FOR PRINTED THAI CHARACTER RECOGNITION 7 3. EXPERIMENTAL RESULT The system has been developed by using the MS Visual Basic 5.0 on Windows 98. The computer we used is Cyrix 6X CPU with 80 Mbyte EDO RAM. To enhance speed and accuracy, based on the idea that most documents are usually typed with only one font, only that font in training set will be selected as the system knowledge base. The recognizing speed is, then, about 5 character images per second with 97.44% correctness. The system was trained with the character images of the font AngsanaUPC, BrowalliaUPC and CordiaUPC. The testing set consists of character images of the same fonts as those of the training set. The total number of all testing patterns are 092 character images. These character images are obtained by scanning the practical documents that have a comparable image quality as those of the training set. The scanning resolution is 300 dpi. The experimental result in Table obviously shows that classification based on multiple feature gives more accuracy than using only one feature information. Table The Experimental Result Feature Extraction Percent of the Correct Result direction of contour density peripheral information all features CONCLUSION AND FUTURE WORK This paper presents a simplified and applicable Thai OCR system which based on multiple feature extraction and minimum distance classifier. The character images that can be recognized correctly are the normalized images of which the black pixels are in the same region as that of the learned characters in the knowledge base. Accordingly, the handwritten characters which have the same characteristics as mentioned above, can also be recognized correctly. Figure 5 shows the examples of unseen scribbly Thai handwritten characters that can be recognized by using the knowledge base of the printed character of font Angsana + Browallia + Cordia. For the future work, the system will focus on the extracted feature information from the character image region that can discriminate its category, for example, the image of 'ด' and 'ต' should focus on the upper region. In addition to extracting more feature, the post processor based on [4] will be integrated in order to enhance the performance of the system.

8 8 SNLP 2000, CHIANG MAI, THAILAND Figure 5 The unseen scribbly Thai handwritten characters that were correctly recognized. REFERENCES CHORUENGWIWAT, P., JITAPUNKUL, S., WUTTISITTIKULKIJ, L., and SEEHAPAN, P Distinctive Feature Analysis for Thai Handwritten Character Recognition Based on Modified Stroke Changing Sequence. In Proceedings of the 998 IEEE Asia-Pacific Conference on Circuits and Systems, Thailand, COOPER, D Fuzzy Letters and Thai Optical Character Recognition. In Proceedings SNLP 95 The 2 nd Symposium on Natural Language Processing, Thailand, DING, X. 997., Machine Printed Chinese Character Recognition. Handbook of Character Recognition and Document Image Analysis, WSPC, KAWTRAKUL, A A Computational Model for a Writing Production Assistant. In Proceedings NLPRS 95 Natural Language Processing Pacific Rim Symposium'95, Seoul, KIJSIRIKUL, B., SINTHUPINYO, S., and SUPANWANSA, A Thai Printed Character Recognition by Combining Inductive Logic Programming with Backpropagation Neural Network. In Proceedings of the 998 IEEE Asia-Pacific Conference on Circuits and Systems, Thailand, LURSINSAP, C Speeding Up Handwritten Character Neural Learning by surface Projection. In Proceedings SNLP 95 The 2 nd Symposium on Natural Language Processing, Thailand, PHOKHARATKUL, P., and KIMPAN, C Printed Thai Characters Recognition Using Hough Transform Method. Ladgrabang Engineering Journal, Vol. 3 No. 2, PHOKHARATKUL, P., and KIMPAN, C Recognition of Handprinted Thai Characters Using the Cavity Features of Character Based on Neural Network. In Proceedings of the 998 IEEE Asia-Pacific Conference on Circuits and Systems, Thailand, SUPAPA, S Probability and Statistic for Engineer. Physics Center, Bangkok. TAEWIT, T., and PIN-NGERN, A Pattern Recognition Using Fuzzy Similarity Relations. Ladgrabang Engineering Journal, Vol. 3 No. 2, 0-9. TANPRASERT, C., and TANPRASERT, T Variable Simulated Light Sensitive Model for Handwritten Thai Digit Recognition. In Proceedings SNLP 95 The 2 nd Symposium on Natural Language Processing, Thailand, THUMWARIN, P., and CHITTAYASOTHORN, S An Object-oriented Expert System for Thai Character Recognition. In Proceedings of the 998 IEEE Asia-Pacific Conference on Circuits and Systems, Thailand,