2009 IEEE International Advance Computing Conference (IACC 2009)Patiala, India, 6-7 March 2009

"Analysis of Directional Features - Stroke andContour for Handwritten Character Recognition"

Supriya Deshmukh, Leena RaghaComputer Engineering Department,

RAIT, Mumbai UniversityDr. D.Y. Patil Vidyanagar,,Nerul, NaviMumbai -400706,

supriya_deshmukh@rediffmail.com,lrragha(rediffmail.com

Abstract-Machine simulation of human functions like classes. Based on these examples the machine builds arecognition of the text is a challenging task. The Off-line prototype or a description of each class of characters. Then,Handwritten Character Recognition requires more research to during recognition, the unknown characters are compared toreach the ultimate goal of machine recognition of the text. An the previously obtained descriptions, and assigned the class thatattempt is made towards English language by a large number of gives the best match.researchers since six decades. But for Indian languages it is still adream. We propose a method on offline isolated English In this paper, we introduced the concept of Handwrittencharacter. The method is also applied to Marathi vowels. The Character Recognition (HCR) system based on stroke lengthimage acquired is preprocessed to remove all unwanted details and contour direction as two different set of features tofrom the image so that the image is suitable for feature recognize the isolated characters. We propose two differentextraction. Feature extraction plays an important role in classification techniques based on correlation i.e. Dissimilarityhandwritten recognition. The two feature extraction methods correlation and Similarity correlation.based on directional features are considered. The first methoduses stroke distribution of a character. The second method uses The rest of the paper is organized as follows: Section 2contour extraction. The Two directional features are compared discusses related work done in directional features. Section 3with two different correlation techniques separately to check the discusses the proposed system architecture. Section 4 andsuitability of the recognition method. First correlation technique Section 5 presents the Experimental results and Conclusionscalculates the dissimilarity between reference pattern and test respectively.pattern, and the other calculates the similarity between referencepattern and test pattern. The result of the comparison is to II. LITERATURE SURVEYclassify the character under consideration to a class if hit. If miss,the confusion information is extracted for the analysis. There are varieties of feature extraction techniques

available for handwritten character recognition. Here we haveKeywords- OCR, HCR, directional features, similarity considered statistical feature extraction method, which uses

correlation, dissimilarity correlation. directionality feature of the input character. The directionalfeature appeared in the end of 1970s.Initially the work was

I. INTRODUCTION dealing with handwritten digit recognition. Recognition hasbeen attempted for the characters of several languagesOptical Character Recognition (OCR) is a process of including Chinese, Japanese, and Korean. The directional

automatic computer recognition of characters in optically feature extraction decomposes the character image intoscanned and digitized pages of text [12]. OCR is most directional planes, each recording the local stroke componentfascinating and challenging areas of pattern recognition with in a specific direction. Some of the features that have been usedvarious practical applications potentials. The main principle in in Chinese character recognition are line direction, strokeautomatic recognition of images is to make the machine lear length, tangential direction and crossing count [3]. Hiromichithe patterns of the shape of the characters. OCR can be .classfiedms on-lie ohar off-le cr bacse On t eda Fujisawa and Cheng-Lin Liu have discussed many features thatclassified as on-line or off-line OCR based on the data can be used for character recognition like contour chaincode,acquisition process It is also classified based on whether text is gradient direction, Kirsh masks, Sobel operators, Gabor filtermachine-printed or handwritten [10]. The Handwritten etc [4]. Lian-Wen Jin et al considered five differentCharacter Recognition problem also called as HCR problem. decompositional algorithms that work on either skeleton or

In HCR the input comprises of images of letters, numbers contour of an image [5][6]. The different combinations of theand some special symbols like commas, question marks etc, features resulted in 9100 average recognition rate of Chineseforming words, sentences, and finally text. With analytic characters. In [2], feature extraction step uses directionapproach, we break the given input into isolated characters for histogram of the contour of an image resulting 93.20%orecognition. The teaching of the machine is performed by recognition rate. In [5] three kinds of decomposition methodsmaking the machine learn the characters of all the different are proposed namely, AND decomposition strategy, OR

978-1T-4244-1888-6/08/f$25.OO Q 2008 IEEE 1114

decomposition strategy and EDGE decomposition strategy be successfully used to remove the noise on the documentworked on Chinese character with recognition result 98%. In images due to low quality of paper and ink, as well as erratic[13] five kinds of line-density based elastic meshing methods hand movement. We have performed bridge, clean, fill,are used to extract the features with the recognition rate of majority sequentially to remove noise from the scanned92.710%. N. Sharma, U. Pal and F. Kimura have worked on images. The feature extraction using contour method we haveKannada numeral using histograms of chain code of the also performed thinning.contour points which results in average 99% recognition rate 3)Normalization: Normalization removes the unnecessary part[7]. from the character image and brings it into specific size, same

as reference pattern. We have followed two steps inIII. PROPOSED METHODOLOGY normalization.

Since the Handwritten Character Recognition problem is a) Segmentation. which removes the un-necessary part fromvery complicated, a layered architecture is considered, so that the character image.every layer function is well defined and can be modified b) Size normalization:which brings the reference pattern andwithout affecting other layer. The handwritten character test patter into specific size. In our case, we are using size ofrecognition can be grouped in four stages [8][9] as shown in the pattern as 64 x 64.figure 1.

C. Feature Extraction

Wnput: After normalizing the character image into fixed size,directional planes are generated to record the local stroke'FeZ:tltt5:trdea.EI* Ertasznng components in a specific direction. The decomposition of thecharacter image into directional planes using stroke direction

,wilip-t can be done in several ways [3][4][6]. Line direction, strokesegment direction, contour chain code, gradient, crossing

Figure 1. Stages in OCR count, tangential direction etc. are some of the techniques usedby different researchers.

A. Input We have implemented the following two distinct featuresThe input given to handwritten character recognition is extraction techniques namely: directional features using

optically scanned character image. We are considering Offline extended peripheral direction contributivity and directionalIsolated Characters. For the experiments the English Uppercase features using contour directional angle.characters, collected from ETL6 database is considered. That iswe have 26X200 character set of English ETL6 database. We 1)Extended Peripheral Direction Contributivity: The methodalso gathered 100 samples of English character set collected uses Stroke feature distribution on the two-dimensional plane,from 10 users. And thus forming 26X100 character set of which is called as Local Direction Contribution [11]. At eachEnglish collected locally. For Marathi, 100 samples of Vowels black point in the character image, the eight quantizedare considered, collecting 12X100 size of the character set of directions are taken as shown in figure 2(a) On each contourMarathi. black pixel the run length li in each of the eight directions is

computed. The distance is measured along each direction fromB. Preprocessing the each point on the thick boundary to the exterior boundary

Scanned documents give images of text which needs to be point. These distances are denoted as li (i = 1,2,..., 8). At eachsegmented into words, characters, and number of techniques foreground pixel 3 x 3 mask is created as shown in 2(b). Theavailable for the same. Since we are giving weightage for length of all the four stroke is calculated and filled in 3 x 3feature analysis and recognition techniques, we assumed that mask.the text is segmented into characters and then used for testing.

Since the scanned character images often contain noise thatarises due to ink flow, ink spread, background noise,digitization imperfections etc. Therefore, it is necessary to filter lI_this noise before it is used for feature extraction. We have l Iperformed a number of preprocessing operations in sequence as *listed below:

1)Binarization:Binarization is a technique in which any grayscale converts image to binary image. In any image analysis or (a) (b)enhancement problem, it is very essential to identify the objectsof interest from the rest. Figure 2. Stroke Length Extraction (a) Input Image Peripherally Scanned In2)Morphological operation. There are number of 8-Directions (b) Enlarged Part Of The Image Normalized To Four Directions.Morphological operation, which is used for connecting brokenstrokes, eliminating small breaks and holes, reducing the widthof the line to some extent. The morphological, operations can

2009 IEEE International Advance Computing Conference (IACC 2009) 1:115

The opposite distance values are summed up. The direction standard set of stored prototypes. We have used two types ofcontributivity di (i = 1,2 ... 4) is computed according to the classification techniques:following definition:

1) Dissimilarity Correlation. This classification methodcalculates the dissimilarity between the input image with all the

)2=4 classes of characters in the database.The input character is thend = (l i + I+4) / Q(i + li+4 ) (1) assigned to class c that gives minimum distance.

i=l

i=4x64x64The four-dimensional distance vector is defined at each dissimi (a, b)= minm abs(aj [i]-b[i]) (4)

pixel. These four feature vectors correspond to four directions =L ii.e. horizontal, vertical, left diagonal and ight diagonal figure.

2)Contour Directional Angle: The contour is traced counter where a represents feature vector of trained pattern.anti-clockwise and expressed as an array of contour elements. b represents feature vector of input patter.The transitions from background to foreground are detected. Here the difference is calculated between two patternsEach contour element represents a pixel on the contour. For along the four dimensions. Hence i ranges from 1 to 4X64X64each point on the contour, directional features are extracted i.e.16384.according to its directional angle [6]. The slope of two points 2)Similarity Correlation. This classification method calculatesgives the angle between the line formed by two neighbouring the similarity between the input image with all the classes ofpixels. The slope between these two points is calculated by characters in the database.The input character is then assignedequation 2. to class c that gives maximum similiarity.

0= tan-' [(x2 -xl)/(y2 -yl] (2) Fj=4x64x64(abi1

The angle for a point is calculated by its slope from pre pre Simi (a, b) = maxj=l:c (5)and post post point as shown in figure3.

where (at, bi) represents the inner vector multiplication.1ai 11.|bib| represents the euclidean norm of that vector.

( ly) (2,y) (2,y2) E. OutputThe output of the handwritten character recognition is

Figure 3. The Given Point (X,Y) With Its Pre pre and Post post Points. generally ASCII code of that alphabet. The recognition willproduce textual representation of a given character. In the

Depending on the angle, 4 -dimensional features are experiment, we have used the output to find the recognitionextracted for each point in contour. Each dimension represents rate if classified correctly. If not classified correctly, then todirectional feature in each direction namely, horizontal, analyze the reason for the misrecognition, we are creating thevertical, left oblique and right oblique, confusion matrix.

The resulting image is too thin to use for our classificationtechniques. Thus we need to smooth the image by considering IV. EXPERIMENTS AND RESULTSnearby pixels for better recognition. Following Gaussian The methods implemented are tested and analysed in fourfunction is used to blur the direction features: different combinations:

2X2+Y2 1) Feature Extraction using Stroke Length with(x2+y ) Dissimilarity Correlation (SD).

e (3) 2) Feature Extraction using Stroke Length with SimilarityHere, we have assumed that value for

2is 4. The blurring Correlation (SS).

produce by the gaussian function must affect the nearest pixel, 3) Feature Extraction using Contour Directional Angle withso the blurring window is of size 5x5. Dissimilarity Correlation (CD).

D. Classification 4) Feature Extraction using Contour Directional Angle withTo compare the unknown pattern with reference pattern, we Similarity Correlation (CS).

are considering template matching as a classifier. There are Table I summarizes the recognition rate for two differentmany template matching techniques [9]. Here we are feature extraction method with two different classifIcationconsidering direct matching technique. In this method, a gray- techniques performed on different script for varying databaselevel or binary input character is directly compared to a size.

111116 2009 IEEE Internactionalz Advance Computing Conference (IACC 2009)

TABLE I. AVERAGE RECOGNITION RATE OF DIFFERENT TECHNIQUES times these characters are badly misrecognized for fewUSING DIFFERENT DATABASES. alphabets. Thus we have collected English character set from

Script Database Feature . Recogni- different users locally to verify the methods.Size Extraction tion Rate

EnglIsh Stroke length Dissimilarity 83.88 %0 Figure 5 shows the plot of the four techniques for EnglishEngls 200 Similarity 93.29 % character set collected from 1O different users. Some of the bad

patteE6s Contour Dissimilarity 85.60 % misrecognition occurred previously eliminated using thisdirectional Similarity 91.71 % database. Stroke and Contour method behaves in the sameangle fashion for both the classification techniques.Stroke length Dissimilarity 88.04 %

English 100 Similarity 94.27 %User Contour Dissimilarity 87.54 %120Samples p directional Similarity 93.69 %0

angle 10nn_anglke length Dissimilarity 77.33 0o10%Marathi 100 Similarity 85.42 % ok DissimilartyMaerah 10 Contour Dissimilarity 51.170 roSamples Pietonal Stroke Sim larltypatterns directonale Similarity 85.17 00 60L Cotoi Dssmlarty

40 ContoLt Sim ilarity

Figure 4 shows the plot of the four different techniques for 20 -

English character set. The number of matches of each characterfor a particular method is realized. Stroke Similarity method 0-works better than the other methods for approximately all the 1 3 5 7 9 11 13 15 17 19 21 23 25classes of the characters.

Figure 5. Comparison Of The Methods On English User Database.250

Table III shows the confusion matrix computed for each200 method on English user database and we have observed

-Strke issiilaityreduction in misrecognition

15 __1 |/ | Stroke Dissimilarity150 I-\ -Stroke SimilarityContour Dissimilarity TABLE 111. CONFUSION MATRIX FOR ENGLISH USER DATABASEContour Similarity

Method Cut off lop 3 misrecognition1 2 3

50 SD 80% B/E,l E/F, L

SS 90% I/J, T K/H, R S/B0| CD 80% | D/O E/L,P Q/O,G

1 3 5 7 9 11 13 15 17 19 21 23 25 CS 90% J/1 K/R, X T/J, I

Figure 4. Comparison Of The Methods On English For ETL6 Database. Figure 6 shows the plot of the four techniques for Marathicharacter set. The number of matches of each vowel for all the

Table II shows the confusion matrix computed for each methods can be visualized. Similarity classifier for both themethod, considering certain cut off for English ETL6 database. feature extraction methods works equally considering itse.g. First row indicates that by considering 75% cut-off, the average recognition rate.feature extraction method using Stroke Length withDissimilarity Correlation gives confusions like E is mostlymisrecognised as F or L and like wise. 120

100 TTABLE II. CONFUSION MATRIX FOR ENGLISH ETL6 DATABASE

-.-._.

Method Cut off Top 3 Confusion1 2 3 0 ______rO2Sidy

SD 75% E/F, L R/P, K V/Y, o AI Dis111 - -ySS 90% | E/F, L I/T, J O/D, B | S I>tyCD 75% E/F, L I/T, J R/P, KCS 90%o E/F, L F/B, P O/D, J | | 2D ..l

The most of the time misrecognition is due to the same 1 2 3 4 s 6i 7 8 9 0ii1topological structure. There are some peculiarities for some !____________________________characters like 0, V, Z are written as 0, V, i. Hence some

Figure 6. Comparison Of The Methods On Marathi User Database.

2009 IEEE Inxternational Advanxce Computing Conference (IACC 2009) 1:117

Table IV shows the confusion matrix computed for each [2] Cheng-Lin Liu,In Jung Kim, and Jin H. Kim, "High Accuracymethod, Marathi character set. Here we have numbered vowels Handwritten Chinese Character Recognition by Improved Featuresequentially as 1, 2... 12.1f the characters are not written with Matching Method",pp.1033-1037,1997sufficient stress then it results misrecognition. e.g. gT may [3] Ching Y. Suen, Shunji Mori, Soo H. Kim and Cheung H. Leung,misrecognised,by alphabet,,, because ,of the less stress in the"Analysis and recognition of Asian scripts- The state of the Art",misrecognised by alphabet D because of the less stress in th e Proceedings of the seventh Intel. Conference on Document analysis andright part of T. recognition (ICDAR), pp. 866-878,2003.

[4] Hiromichi Fujisawa and Cheng-Lin Liu, "Directional Pattern Matchingfor Character Recognition Revisited", Proceedings of the seventh Intel.

TABLE IV. CONFUSION MATRIX FOR MARATHI USER DATABASE Conference on Document analysis and recognition (ICDAR), pp. 794-Method Cut off l~~~~op3 confusion 798, 2003.

Method Cut off Top 2 3 [5] Lianwen Jin and Gang Wei, "Handwritten Chinese CharacterSD 80% 1/2, 6 10/11, 12 12/2, 1 Recognition with Directional Decomposition Cellular Features", JournalSS 90%o 4/3 7/11 9/11 of Circuits, Systems, and Computers, Vol. 8, No. 8, pp: 517-524, 1999.CD 60% 2/11,1 5/11 8/11 [6] Lian-Wen Jin, Jun-Xun Yin, Xue Gao, Jiang-Cheng Huang, "Study ofCS 90% 9/10, 1 11/1, 4 12/1,2 several directional features extraction methods with local elastic

meshing technology for HCCR", Proceedings of the Sixth Intel.V. CONCLUSIONS Conference for Young Computer Scientist, 2002.

Recognition of handwritten characters has been a popular [7] N. Sharma, U. Pal and F. Kimura, "Recognition of HandwrittenKannada numerals", Ninth Intel. Conference on Information technology

research area for many years because of its various application (ICIT), pp. 133-136, 2006.potentials. Feature Extraction is one of the most important step [8] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview of Characterin the field of handwritten character recognition. The Recognition focused on offline handwriting", IEEE Transactions onDirectional algorithm for feature extraction is considered to be Systems, Man, and Cybernetics, vol. 31,pp216- 233,2001.efficient method. Two kind of directional features are [9] Oivind Due Trier, Anil K. Jain and Torfinn Taxt, "Feature Extractionexamined, one by using stroke length distribution method and Methods for Character Recognition A Survey", Pattern Recognition,other by using contour. The two different correlation voL 29, pp 641-662,1996.techniques . i t , .. ...,andsimilarity have been [10] Rejean Plamondon, and Sargur N. Srihari, "On-line and Off-line

techniques dissimilarity Handwriting Recognition: A comprehensive Survey", IEEEexperimented. Transactions on pattern analysis and machine intelligence, vol. 22, No.

We have observed that the Stroke length method gives 1, January 2000.[11] Toru Wakahara, Yoshimasa Kimura and Mutsuo Sano, "Handwritten

Japanese Character Recognition Using Adaptive Normalization bycontour method behaves well on a character having curves. Global Affine Transformation", International Conference on DocumentSimilarity measure method used for classification behaves well Analysis and Recognition, pp. 424-428,2001.than dissimilarity measure. When patterns are given small [12] U.Pal, B.B.Chaudhari, "Indian script character recognition: a survey",shape variations the change of similarity measure is small. Pattern Recognition, vol.3 7, pp.1887-1899, 2004.Dissimilarity measure gives straight-line distance between two [13] Xue Gao, Lian-Wen Jin, Jun-Xun Yin, Jiang-Cheng Huang," A Newinput vectors which does not result in a efficient classification Stroke-Based Directional Feature Extraction Approach for Handwrittentecniqe.However it is time efficient and saves a lot of Chinese Character Recognition", Proceedings. Sixth Internationaltechnique. nost Conference, pp635 - 639,2001.

computational cost.

REFERENCES[1] Anuradha Srinivas, Arun Agarwal, and Raghavendra Rao,"An Overview

of OCR Research in Indian Scripts", IJCSES International Journal ofComputer Sciences and Engineering Systems, Vol: 2, pp: 18-23,April2008.

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