Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach

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1 Handwrtten Devanagar Word Recognton: A Curvelet Transform Based Approach Brjmohan Sngh Department of CS&E College of Engneerng Roorkee Roorkee , Inda Ankush Mttal Department of CS&E College of Engneerng Roorkee Roorkee , Inda M.A. Ansar Department of Electrcal Engneerng Galgotas College of Engneerng and Technology Gr. Noda, Inda Debashs Ghosh Department of E&C IIT Roorkee Roorkee , Inda Abstract Ths paper presents a new offlne handwrtten Devanagar word recognton system. Though Devanagar s the scrpt for Hnd, whch s the offcal language of Inda, ts character and word recognton pose great challenges due to large varety of symbols and ther proxmty n appearance. In order to extract features whch can dstngush smlar appearng words, we employ Curvelet Transform. The resultant large dmensonal feature space s handled by careful applcaton of Prncpal Component Analyss (PCA). The Support Vector Machne (SVM) and k-nn classfers were used wth one-aganst-rest class model. Results of Curvelet feature extractor and classfers have shown that Curvelet wth k-nn gave overall better results than the SVM classfer and shown hghest results (93.21%) accuracy on a Devanagar handwrtten words set. Keywords- OCR; Devanagar; Curvelet Transform; SVM; k-nn I. INTRODUCTION Many of the challenges n Optcal Character Recognton (OCR) research are rased n the area of handwrtten character and word recognton. Real-world handwrtng s a mxture of cursve and noncursve parts, whch makes the problem of recognton sgnfcantly dffcult. Offlne handwrtten word recognton s an mportant area of Document Analyss and Recognton (DAR). DAR s a mechansm n whch the document mages are processed to obtan text and graphcs features. The man objectve of DAR s to read the ntended nformaton from the document usng computer as much as a human would do. The outcome of DAR system s usually n the ASCII format [1]. The applcatons of DAR [2-3] nclude such as offce and lbrary automaton, publshng houses, help to the vsually handcapped when nterfaced wth a voce syntheszer, postal servce assstance, readng entrance examnaton forms, processng of applcatons of vctms and crmnal records n polce staton, etc. A slght mstake n nterpretng the characters can lead to mstake n the automaton process such as wrong dspatch n postal servce or wrong entry n entrance examnaton forms. Handwrtng recognton can be acheved by character, word and sentence level. A character recognzer needs to be traned wth sample characters from the alphabets used n the language. There are two approaches for the recognton of solated handwrtten Devanagar words [4]. The frst s to segment the word nto ts character parts, ndvdually recognze each character, and then reconstruct the word. The major drawback of ths approach ISSN : Vol. 3 No. 4 Apr

2 for the Devanagar scrpt s that the words contan Matra, Shrorekha, conjunct characters, modfers and lack of standard benchmark database for tranng the classfer. The second scheme s to recognze the word n ts entrety. Word recognzers are complex f they are general purpose but are smpler f t s based on specfc lexcon. Ths approach of word recognton avods the overhead of character segmentaton. Whle sgnfcant advances have been acheved n recognzng Roman based scrpts lke Englsh, deographc characters (Chnese, Japanese, Korean, etc) and Arabc to some extent, OCR research on Indan scrpts s very less. Only few works on some of the major scrpts lke Devanagar, Bangla, Gurumukh, Taml, Telgu, etc. are avalable n the lterature. The era of handwrtten Devanagar character recognton was started n the early days of OCR research by Seth et al. [5]. The research n offlne Devanagr word recognton was started by Paru et al. proposed a HMM based holstc approach for the word recognton [4]. Later, Shaw et al. publshed a segmentaton based approach [6]. Recently, a Curvelet-based SVM recognzer has been proposed n [7] for recognton of handwrtten Bangla characters wth an overall accuracy of 95.5%. Snce, Devanagar and Bangla belong to the same Brahmc famly of scrpts havng a common orgn; many smlartes are observed among ther characters. Consequently, ther characterstc features are somewhat close to each other and hence, many character recognton algorthms are expected to be equally applcable to Devanagar, Bangla and other scrpts belongng to the Brahmc famly. In vew of ths, we propose a Curvelet based feature extractor wth SVM and k-nn classfers for offlne handwrtten Devanagar word recognton system. In our present work for word recognton, we have appled the holstc approach to avod the overhead of segmentaton and due to lack of standard benchmark database for tranng the classfer. Snce a standard benchmark database was not avalable for Indan scrpt so we created a word database for Devanagar to test the performance of our system. In the present report, tranng and test results of the proposed approach are presented on the bass of ths database. II. FEATURES OF DEVANAGARI SCRIPT Devanagar s the scrpt used for wrtng Hnd whch s the offcal language of Inda [8]. It s also the scrpt for Sanskrt, Marath and Nepal languages. Devanagar scrpt conssts of 13 vowels and 33 consonants characters. These characters are called the basc characters. The characters may also have a half form. A half character n most of the cases touches the followng character, resultng n a composte character. Some characters of Devanagar scrpt take the next character n ther shadow because of ther shape. The scrpt has a set of modfer symbols whch are placed ether on top, at the bottom, on the left, to the rght or a combnaton of these. Top modfers are placed above the shrorekha (Head lne), whch s a horzontal lne drawn on the top of the word. The lower modfers are placed below the character whch may or may not touch the characters. More than one lower modfer may also be placed below one character. A character may be n shadow of another character, ether due to a lower modfer or due the shapes of two adjacent characters. Upper and lower modfers wth basc character modfers make OCR wth Devanagar scrpt very challengng. OCR s further complcated by compound characters that make character separaton and dentfcaton very dffcult. III. STEPS INVOLVED IN WORD RECOGNITION Ths paper attempts to presents a method, whch s based on the followng mportant steps: Pre-processng, Curvelet based feature extracton and classfcaton by SVM and k-nn. Fgure 1 shows the archtecture of proposed system. 3.1 Pre-processng In the off-lne OCR, handwrtten mage to be recognzed s captured by a sensor, for example, a scanner or a camera. Pre-processng of grayscale source mage s essental for the elmnaton of nosy areas, smoothng of background texture as well as contrast enhancement between background and text areas. For smoothng, the nput gray level mage s frst fltered by the Wener flter [9] and then bnarzed by the Otsu s method [10]. The Wener fltered grayscale mage I s obtaned from source grayscale mage Is accordng to formula: ISSN : Vol. 3 No. 4 Apr

3 2 2 v I x, y x, y s I (1) 2 Where s the local mean, 2 s the varance at 3 3 neghbourhood around each pxel and 2 s the average of all estmated varance for each pxel n the neghbourhood. 3.2 Feature Extractons After pre-processng the mages, features relevant to the classfcaton are extracted from the smoothed mages. The extracted features are organzed n a database, whch s the nput for the recognton phase of the classfer. A feature extracton scheme based on dgtal Curvelet transform [11] has been used. In ths work, the words from the sample mages are extracted usng conventonal methods. A usual feature of handwrtten text s the orentaton of text wrtten by the wrter. Each sample s cropped to edges and reszed to a standard wdth and heght sutable for dgtal Curvelet transform. The dgtal Curvelet transform at a sngle scale s appled to each of the samples to obtan Curvelet coeffcents as features. In our case, we obtaned 1024 (32 x 32) feature coeffcents, dependng on the sze of the nput sample mage. Output (Recognton) Classfcaton by SVM and k-nn Feature Extracton by Curvelet Transform Bnarzaton by Otsu s Approach Smoothng by Wener flter Input (Grey Scale Image) - Isolated Word Image Curvelet Transform Fgure 1: An archtecture of proposed OCR Word recognton earler was handled by strng edt dstance [4] and scalar features [6]. However, for large set of characters, as n Devanagar language, automatc curve matchng s hghly useful. Consderng ths, we explored the use of curvelet transform whch represents edges and sngulartes along curves more precsely wth the needle-shaped bass elements. The elements own super drectonal senstvty and smooth contours capturng effcency. Snce Curvelets are two dmensonal waveforms that provde a new archtecture for multscale analyss, they can be used to dstngush smlar appearng characters better. ISSN : Vol. 3 No. 4 Apr

4 f P f f,, 0, 1 2 f Fgure 2: Elements of Curvelet The Curvelet frame preserves the mportant propertes, such as parabolc scalng, tghtness and sparse representaton for surface-lke sngulartes of co-dmenson one. Fgure 2 shows a sample of dgtal Curvelet. Snce many of the characters n a word not only consst of edge dscontnutes but also of curve dscontnutes. The most wdely used Wavelet transform works well wth edge dscontnutes but a curve dscontnuty affects all the Wavelet coeffcents. On the other hand, the curve dscontnutes n any character or word are well handled wth Curvelet transform wth very few numbers of coeffcents. Hence, Curvelet-based feature are lkely to work well for Devanagar character and word recognton. The curvelet transform ncludes four stages: 1. Sub-band decomposton 2. Smooth parttonng 3. Renormalzaton 4. Rdgelet analyss 1. Sub-band decomposton s to dvde the mage nto resoluton layers where each layer contans detals of dfferent frequences.e., Here, P0 s the low pass flter and Δ1, Δ 2 are hgh pass (band pass) flter. 2. Smooth Parttonng: Each subband s smoothly wndowed nto squares of an approprate scale.e. Q S f Q Q S (2) s f (3) Where, Q s denotes the dyadc square of sde 2 -s and ω be a smooth wndowng functon wth man support of sze 2 -s 2 -s. 3. Renormalzaton: Each resultng square s renormalzed to unt square.e. g Q 2 s T Q QS f, Q QS 1 (4) 4. Rdgelet analyss: Each square s analyzed n the ortho-rdgelet system.e Dmensonalty Reducton g Q, p, Q (5), A sgnfcant problem n usng Curvelet transform s that t gves a large dmensonal feature space. Any classfer wll requre a lot of tranng data when the feature space s large as well as t wll be tme consumng. Dmensonalty reducton s therefore an obvous choce. There are several methods of dmensonalty reducton. Some methods such as [12] [13], select a few promnent features out of all the features. Others lke PCA transform the feature space nto a reduced set of features preservng the nformaton as far as possble [14]. Snce Curvelet s a mathematcal tool whch ISSN : Vol. 3 No. 4 Apr

5 generates features. PCA s a natural choce. PCA provdes a way to dentfy patterns n data and expressng the data n order to hghlght the correlatons such as smlartes and dssmlartes. The frst few egen values from PCA wll contan most amount of nformaton n the present problem, whch does not contan dense nformaton. Thus, we chose to use 200 numbers of egen values for PCA from orgnal 1024 features. These features covered 95% varance n feature space. 3.4 Classfcaton The man task of classfcaton s to use the feature vector provded by the feature extracton algorthms to assgn the object to a category. A more general task s to determne the probablty for each of the possble categores. The abstracton provded by the feature extractor representaton of the nput data enables the development of a largely doman-ndependent theory of classfcaton. The degree of dffculty of the classfcaton problem depends on the varablty n the feature values for object n the same category relatve to the dfference between feature values for objects n dfferent categores. The varablty of feature values for object n the same category may be due to complexty, and may be due to nose [15] SVM Support vector machnes (SVM) was developed by Vapnk n 1995 [16] and t s an extensvely used tool for pattern recognton due to ts many attractve features and promsng emprcal performance specally n classfcaton and nonlnear functon estmaton. SVM are used for tme seres predcton and compared to radal bass functon network. The classfcaton problem can be restrcted to consderaton of the two-class problem wthout loss of generalty. Consder an example of lnearly separable classes. We assume that we have a data set D x l, y 1 (6) of labeled example, n where x, y 1,1, wth a hyperplane, w, x b 0, and we wsh to select, among the nfnte number of lnear classfers that separate the data, one that mnmzes the generalzaton error, or at least an upper bound n t. Hyperplane wth generalzaton property s the one that leaves the maxmum margn between the two classes where, margn s defned as the sum of the dstances of the hyperplane form the closest pont of two classes. If vector s set s separated wthout error and the dstance between the closest vectors to the hyperplane s maxmum then t s sad to be optmally separated. There exsts some redundancy n above equaton, and wthout loss of generalty. It s best to consder canoncal hyperplane, where the parameters w, b, are consdered by, mn w, x b 1. (7) A separatng hyperplane n canoncal form must satsfy the followng constrants, y w, x b 1, 1,..., l. (8) The dstance d (w,b,x) of a pont x from the hyperplane (w,b) s, d w, x b (9) w w, b, x. Hence, the hyperplane that optmally separates the data s one that mnmzes 1 2 w w (10) 2 ISSN : Vol. 3 No. 4 Apr

6 If the two classes are non-sharable the SVM looks for the hyperplane that maxmzes the margn and that, at the same tme mnmzes the quantty proportonal to the number of msclassfcaton error. The performance of SVM classfcaton s based on the choce of kernel functon and the penalty parameter C. In ths work, we used RFB kernel that maps nonlnearly samples nto a hgher dmensonal space, and can handle the case when the relaton between class labels and attrbutes s nonlnear. The RBF kernel can be descrbed as k 2 x, z exp x z (11) Thus, whle usng the RFB kernel functons; there are two parameters C and γ that need to be selected. Usually these parameters are selected on a tral or error bass. In our experment, we used SVM classfer wth Radal Bass Kernel for classfcaton as t has gven best results for our dataset. To obtan a more accurate model, the cost factor C of SVM was adjusted. In our case, cost factor C=20, gave the most desrable results. In order to keep the model smple, the cost factor was not further ncreased k-nearest Neghbour The k-nearest Neghbor (k-nn) classfes an unknown sample based on the known classfcaton of ts neghbours [18]. Let us suppose that a set of samples wth known classfcaton s avalable, the so-called tranng set. Intutvely, each sample should be classfed smlarly to ts surroundng samples. Therefore, f the classfcaton of a sample s unknown, then t could be predcted by consderng the classfcaton of ts nearest neghbor samples. Gven an unknown sample and a tranng set, all the dstances between the unknown sample and all the samples n the tranng set can be computed. The dstance wth the smallest value corresponds to the sample n the tranng set closest to the unknown sample. Therefore, the unknown sample may be classfed based on the classfcaton of ths nearest neghbor. k- NN s an nstance-based learnng type classfer, or lazylearnng where the functon s only approxmated locally and all computaton s deferred untl classfcaton. The tranng samples are mapped nto multdmensonal feature space. The space s parttoned nto regons by class labels of the tranng samples. A pont n the space s assgned to the class c f t s the most frequent class label among the k nearest tranng samples. Usually Eucldean dstance s used. The tranng phase of the algorthm conssts only of storng the feature vectors and class labels of the tranng samples. In the actual classfcaton phase, the same features as before are computed for the test sample (whose class s not known). Dstances from the new vector to all stored vectors are computed and k closest samples are selected. The new pont s predcted to belong to the most numerous classes wthn the set. The best choce of k depends upon the samples; generally, larger values of k reduce the effect of nose on the classfcaton, but make boundares between classes less dstnct. A good k can be selected by parameter optmzaton usng, for example, cross-valdaton. The specal case where the class s predcted to be the class of the closest tranng sample (.e. when k = 1) s called the nearest neghbour algorthm. The accuracy of the k-nn algorthm can be severely degraded by the presence of nosy or rrelevant features, or f the features scales are not consstent wth ther relevance. When gven an unknown data, the k-nearest neghbour classfer searches the pattern space for the k tranng data that are closest to the unknown data. These k tranng tuples are the k nearest neghbours of the unknown data. Closeness s defned n terms of a dstance metrc, such as Eucldean dstance. The Eucldean dstance between two ponts or tuples, say, X 1 = (x 11, x 12,, x 1n ) and X 2 = (x 21, x 22,..., x 2n ), s dst n ( X1, X 2) ( X1 X 2) (12) 1 Typcally, we normalze the values of each attrbute. Ths helps prevent attrbutes wth ntally large ranges (such as ncome) from outweghng attrbutes wth ntally smaller ranges (such as bnary attrbutes). Mn-max normalzaton, for example, can be used to transform a value v of a numerc attrbute A to n the range [0, 1] by computng v mn A v' (13) max mn A A IV. EXPERIMENTAL RESULTS In our experment, we collected a dataset of 28, 500 handwrtten Devanagar words of 30 classes collected from 950 dfferent wrters (sample mage s shown n fgure 2). Feature extracton was done on each sample usng Curvelet Transform at a sngle scale. The feature vector thus obtaned (the coeffcents) had a ISSN : Vol. 3 No. 4 Apr

7 dmensonalty of Prncpal component analyss of the coeffcents was done to reduce the sze of feature vector to about 200 dmensons. The rato between tranng and testng samples was mantaned at 75:25 respectvely. The Support Vector Machne (SVM) and k-nn classfers were used wth one-aganst-rest class model. Expermental results of Curvelet feature extractor wth SVM and k-nn classfers have shown that Curvelet wth k-nn gave overall better results than the SVM classfer and shown hghest results (93.21%) accuracy on a Devanagar handwrtten word set. Table 1 shows the comparson of results of Curvelet transform wth SVM and k-nn classfers. Fgure 2 Sample mage of Dataset Table 1: Shows the comparson of feature extractor wth classfers Feature Extractor Curvelet Features Classfers (75:25 Tranng Test Splt) k- NN (%) SVM (%) Error Accuracy Accuracy Rate Error Rate V. CONCLUSION Ths paper descrbes a holstc system of offlne handwrtten Devanagar word recognton. In ths paper, we proposed a Curvelet feature extractor wth SVM and k-nn classfers based scheme for the recognton of handwrtten Devanagar words. The Support Vector Machne (SVM) and k-nn classfers were used wth oneaganst-rest class model. Results of Curvelet feature extractor wth SVM and k-nn classfers have shown that Curvelet wth k-nn gave overall better results than the SVM classfer. ISSN : Vol. 3 No. 4 Apr

8 The proposed scheme was tested only on 28,500 samples of 30 Indan cty names. However, the accuracy of proposed scheme may be enhanced by ncreasng the number of tranng samples and/ or applyng the proposed scheme at dfferent resoluton scheme. Hence, Curvelet Transform proves to be useful n Devanagar word recognton. FUTURE SCOPE Most of the works reported on Indan languages are on good-qualty documents. Elaborate study on poorqualty documents are not undertaken by the scentsts n the development of Indan scrpt OCR. Experments should be made to observe the effect of poor qualty paper as well as nose of varous types, and take correctve measures. REFERENCES [1] S. Marna Introducton to document analyss and recognton, Studes n Computatonal Intellgence (SCI), Vol. 90, pp. 1 20, [2] Y.Y. Tang, C.Y. Suen, C.D. Yan, and M.Cheret, Document analyss and understandng: a bref survey Frst Int. Conf. on Document Analyss and Recognton, Sant-Malo, France, pp , October [3] R. Plamondon and S. N. Srhar, On-lne and off-lne handwrtten recognton: A comprehensve survey, IEEE Trans on PAMI, Vol.22, pp.62-84, [4] Swapan Kr. Paru and Bkash Shaw, Offlne handwrtten Devanagar word recognton: An HMM based approach, LNCS 4815, Sprnger-Verlag, (PReMI-2007), 2007, pp [5] I. K. Seth and B. Chatterjee, Machne recognton of constraned hand prnted Devanagar, Pattern Recognton, Vol. 9, pp , [6] Bkash Shaw, Swapan Kumar Paru and Malayappan Shrdhar, A segmentaton based approach to offlne handwrtten Devanagar word recognton, PReMI, IEEE, pp [7] B.B. Chaudhur and A. Majumdar, Curvelet based mult SVM recognzer for offlne handwrtten Bangla: A major Indan scrpt, Int. Conf. of Document And Recognton, 2007, pp [8] P.S. Deshpande, L. Malk and S. Arora, Characterzng handwrtten Devanagar characters usng evolved regular expressons, n Proceedng of TENCON, 2006, pp [9] A. Jan, Fundamentals of Dgtal Image Processng, Prentce-Hall, Englewood Clffs, NJ, [10] N. Otsu, A threshold selecton method from grey level hstogram, IEEE Trans on SMC, Vol.9, pp.62-66, [11] E. Candes, L. Demanet, D. Donoho and L. Yng, Fast dscrete curvelet transforms, [12] Abraham, B. and Merola, Dmensonalty reducton approach to multvarate predcton, In Esposto Vnz V. et al. (Eds.): PLS and related methods, CISIA, pp [13] De Jong, S. and Kers, H.A.L. Prncpal covarates regresson, Part-I. Theory, Chemometrcs and Intellgent Laboratory Systems, Vol. 14, pp [14] I.T. Jollffe Prncpal component analyss Sprnger seres n statstcs, 2nd ed., NY, 2002, 487 p. 28 llus. ISBN [15] R. O. Duda, P.E. Hart, and D. G. Stork Pattern classfcaton John Wlly publcaton. [16] V. Vapnk, The nature of statstcal learnng theory, Sprnger Verlang, [17] Y. Yang, Expert network: Effectve and effcent learnng from human decsons n text categorzaton and retreval, In Proceedng of 17th. Ann. Int. ACM SIGIR Conference on Research and Development n Informaton Retreval (SIGIR'94), 1994, pp ISSN : Vol. 3 No. 4 Apr