WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

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1 ISSN: (ONLINE) DOI:.297/jvp.24. ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, FEBRUARY 24, VOLUME: 4, ISSUE: 3 WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION P. Mathvanan, B. Ganesamoorthy 2 and P. Maran 3 Department of Electroncs and Communcaton Engneerng, Velammal Engneerng College, Inda E-mal: mathece5@gmal.com 2 Department of Electroncs and Communcaton Engneerng, Adhparasakth Engneerng College, Inda E-mal: bganesamoorthy@gmal.com 3 Department of Electroncs and Communcaton Engneerng, SSN College of Engneerng, Inda E-mal: maranece7@gmal.com Abstract In ths paper we develop a system for wrter dentfcaton whch nvolves four processng steps lke preprocessng, segmentaton, feature extracton and wrter dentfcaton usng neural network. In the preprocessng phase the handwrtten text s subjected to slant removal process for segmentaton and feature extracton. After ths step the text mage enters nto the process of nose removal and gray level converson. The preprocessed mage s further segmented by usng morphologcal watershed algorthm, where the text lnes are segmented nto sngle words and then nto sngle letters. The segmented mage s feature extracted by Daubeches 5/3 nteger wavelet transform to reduce tranng complexty [, 6]. Ths process s lossless and reversble [], [4]. These extracted features are gven as nput to our neural network for wrter dentfcaton process and a target mage s selected for each tranng process n the 2-layer neural network. Wth the several traned output data obtaned from dfferent target help n text dentfcaton. It s a multlngual text analyss whch provdes smple and effcent text segmentaton. Keywords: Slant Correcton, Morphologcal Watershed Algorthm, Daubeches 5/3 Integer-to-Integer Wavelet Transform, Neural Network. INTRODUCTION Handwrtng text mage analyss s usually nvolved n securty purposes of expensve and ancent hstorcal documents. In our practce, handwrtng text mage analyss nvolves preprocessng, segmentaton, feature extracton and wrter dentfcaton. Ths handwrtten mage analyss s used to analyze a document of varous sze and texture. It s a multlngual analyss, text analyss are performed based on two mportant concepts they are text ndependent and text dependent methods. In text ndependent methods more than one wrter has been consdered here we consder ndvdual character as words. But n text dependent the mage s statstcally computed, a wrter has to wrte the dentcal text to perform dentfcaton. In our handwrtten text analyss we use both text ndependent and text dependent method due to ts flexblty. Here the key process s to extract the characterstc from the handwrtng mage quckly and effcently by usng slants removal and watershed segmentaton [, 2]. In ths process of handwrtten text analyss prevously several other technques are employed lke OCR (optcal characterstc recognton) [2], GLCM (Gray Level Cooccurrence Matrx) and GLRL (Gray Level Run Length matrx) and so on. OCR s a multlngual analyss n ths process usually we convert our text mage nto electroncs form. In some case we do texture classfcaton of handwrtten document by usng GLCM and GLRL matrx [5, 7]. A lot of segmentaton s done n the past on handwrtten text mages. The varous exstng methods for segmentaton are categorzed as projecton based, Hough transform based, smearng, groupng, graph based, CTM (Cut text Mnmum) approach, block coverng and lnear programmng, but n our paper we used watershed for segmentaton, ITI WT for feature extracton and neural tool for tranng the gven text mage [, 8]. Our paper s organzed as follows. Secton 2 dscusses the preprocessng stage and n the secton 3 segmentaton process s proposed. In secton 4 feature extracton usng Daubeches borthogonal 5/3 wavelet transform s proposed. In the last secton neural classfer s dscussed. 2. PROPOSED METHOD In the frst stage nput mage s taken from a stored fle or photographed one. In the next Stage preprocessng for nose removal, slant correcton, bnarzaton and normalzaton has been done. In the thrd stage segmentaton process has been done by means of watershed algorthm. In the fourth stage feature extracton of segmented mage has been done by reversble nteger to nteger wavelet transform. In the last stage classfcaton s done by neural classfer. Fg. shows the algorthm for handwrtten text analyss. Input handwrtng text mage Wrter dentfcaton usng neural network Fg.. Block dagram for handwrtten text analyss 2. PREPROCESSING Slant Removal Bnarzaton Preprocessng Feature Extracton Denosng Normalzaton Segmentaton usng Watershed Preprocessng s the process of convertng nput mage nto more sutable form for the future process of any mage analyss. In the preprocessng stage we perform some mportant process lke nose removal, slant removal, grayscale converson, bnarzaton and normalzaton. Fg. shows the varous steps nvolved n the preprocessng []. 767

2 P MATHIVANAN et al.: WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION 2.. Nose Removal: In all photographed or scanned document most common nose s the mpulse nose. It s created due to the lens vbraton and other dsturbances durng scannng or photographng. In our nput mage we have a common nose called salt and pepper or mpulse nose whch affects our handwrtten analyss at a great level for further process of analyss. So n order to remove such unwanted data from our mage we use several flters to remove such nose [, ]. Here we use medan flter to remove mpulse nose shown n the Fg.2 and Fg.3. slant correcton can mprove performance by reducng wthnwrter varaton [2]. Slant correcton s a projecton transformaton useful for regsterng or algnng mages. The use of projecton transformaton as a means to regster an mage wth respect to a dfferent vew ponts. Snce ths mappng s constraned at four 2-D ponts, there are eght coordnates and thus eght degrees of freedom n a projectve transform [3, 9]. It s possble to show that the general form for the 2-D projectve transformaton matrx n a homogeneous coordnate system s gven by matrx & 2 where T s the spatal transform matrx, S s nput mage need to be transformed and S s the resultant mage after slant removal s shown n the Fg.5. Fg.2. Image wth salt and pepper nose occurred to external or nternal dsturbance Fg.3. Image after nose removal usng medan flter 2..2 Slant Correcton: In our text handwrtten analyss we can t judge a person s handwrtng each tme as t vares wth ndvdual as well as wth a person s mnd set whch s best explaned n the Fg.4. Fg.5. Image after slant removal Fg.4. Sheared mage Slant s the angle that the baselne of a word or sentence makes wth the horzontal drecton. Slope can be removed by rotatng the entre text, whle slant s corrected by shearng each word whch means shftng every lne of pxels sdeways by an amount dependng on ts dstance from the baselne. In slant removal 2D spatal transform s used to correct the x data and y data of the gven mage whch automatcally shfts the orgn of your output mage to make as much of the transformed mage vsble as possble [8]. In character recognton process, removng slant n a document wll reduce varaton wthn classes of characters makng those classes easer to recognze. It wll also make character segmentaton easer, snce characters that are straght up have more dstnct space between them than characters that are at a slant. Another applcaton s wrter dentfcaton, where T 2 3 S TS x S TS y x 23 S y xn y N 3x 23 y Bnarzaton and Normalzaton: xn y N xn y N The nose removed RGB mage s converted nto gray mage to make our future process easer. Normalzaton s done to remove the unwanted backgrounds [, 3]. In our handwrtng analyss t also helps n normalzng the space between horzontal and vertcal lnes n text mage, whch helps n segmentng mage nto ndvdual words and letters n our text mage. () (2) 768

3 ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, FEBRUARY 24, VOLUME: 4, ISSUE: 3 3. SEGMENTATION Segmentaton s manly performed to extract the specfed or requred regon from an mage. In handwrtng analyss several segmentaton process have been used. For example, hstogram analyss for segmentaton, OCR and GLCM Matrx are used for handwrtten mage segmentaton [2]. In our analyss we use morphologcal watershed transform to segment a text document. It s more stable segmentaton process and t nvolves n contnuous segmentaton boundares. A grey-level mage may be seen as a topographc relef, whch nvolves catchment basn watershed and watershed lnes. Our proposed Segmentaton s performed based on the watershed lnes []. Gradent Magntude (gradmag) unwanted dark spot n our mage usng expresson (3), where A s a bnary n E and B s a structured element. ΑΘΒ Ζ Ε Β Ζ Α Α Β ΑΘΒ Β Β Ζ b z b Β, Ζ Ε Α Β Α bβ (3) Fg.7. Openng by reconstructed mage Gradent Magntude (gradmag) Fg.6. Edge detecton mage usng gradent magntude Segmentaton process nvolves the boundary detecton regon based processng.e. analyze connected component. Usually pxel vares very rapdly along the boundares between two regons whch were usually analyzed usng gradent magntude method for boundary detecton. In our process of segmentaton we perform background and foreground morphologcal analyss before applyng t nto the watershed transform for segmentaton s shown n the Fg.6. Ths morphologcal process nvolves two mportant process, openng-by-reconstructon and closng-byreconstructon for cleanng up the mage to avod over segmentaton and under segmentaton when applyng t nto watershed transform [6, 9]. 3. MORPHOLOGICAL OPENING In morphology, the basc dea s to probe an mage wth a smple, pre-defned shape, drawng conclusons on how ths shape fts or msses the shapes n the mage. The morphology operators strongly related to Mnkowsk addton whch s shftnvarant operator. Morphologcal openng s smple eroson followed by dlaton whch s used to remove the unwanted structures n an mage. Set theory s usually used to defne the eroson (Θ) and dlaton (). In morphology we regard pxel ntenstes as topologcal hghlghts, whch are shown n the Fg.7. Here openng by reconstructon nvolves eroson followed by ts morphologcal reconstructon whch helps n removng 3.2 MORPHOLOGICAL CLOSING Morphologcal closng s a dlaton followed by eroson whch s used to merge or fll structures n an mage. Closng-byreconstructon nvolves dlaton followed by morphologcal reconstructon whch helps n removng the unwanted components n our background usng expresson (4) where A s a bnary n E and B s a structured element. 3.3 EROSION Α Β Α ΒΘΒ (4) Eroson s usually employed here to reduce objects nto an mage usng the below expresson (5) where A s a bnary n E and B s a structured element. ΑΘΒ Β Ζ Ζ Ε ΒΖ Α b Ζ b Β, Ε where, B Z s denoted as translaton of B by a vector z. Eroson of A by B s also gven by the expresson (6). The output after eroson process s shown n the Fg DILATION bβ b Ζ (5) ΑΘΒ Α (6) Fg.8. Openng or Eroson of the mage Dlaton s employed here to ncrease the object nto an mage usng followng expresson (7) where A s a bnary n E and B s a structured element. Α Β Α (7) bβ b 769

4 P MATHIVANAN et al.: WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION The dlaton s also obtaned by the expresson (8) and ts correspondng output mage s shown n the Fg.9. Α Β z Ε Β z Α Φ 5 Β x Ε Β where, B 5 denotes the symmetry of B Fg.9. Closng or Dlaton of the mage Further apply the watershed transform to segment the handwrtten mage nto letter and words based on watershed lnes obtaned from the above process are shown n the Fg. and Fg.. Fg.. Morphologcal Watershed transformed mage 5 (8) easly reversble and lossless when compared wth other wavelet transform [3]. 4. MORPHOLOGICAL CLOSING DAUBECHIES 5/3 WAVELET TRANSFORMS Daubeches 5/3 borthogonal wavelet s used for 3 level decomposton and the equaton s gven by the Eq.(9), d d s s 2 4 d s d where, d hgh pass s sub band sgnal and s s low pass sub band sgnal [], [4], [7]. The nverse transform to recover lossless the orgnal samples s gven by the expresson (), d d s s s 2 4 d 2 s d s 2 (9) () Above decomposton s performed by decomposng the rows, then by columns for one wavelet decomposton of mage and the same process s repeated for mult-scale wavelet decomposton and are shown n the Fg.. Usually n order to dentfy the characterstcs of handwrtng mage we use statstcal method for analyzng handwrtng mage by calculatng mean and varance of each level of decomposton usng the followng Eq.(). Let f(x, y) be the segmented mage, c (x, y), c 2 (x, y), c 3 (x, y) are the coeffcents of mage at each level of decomposton, mean and varance equatons are gven for frst level of decomposton []. M j V j c x, mean c var x, y y () Fg.. Segmented mage usng watersheds transform 4. FEATURE EXTRACTION Feature extracton s the process of transformng nput data nto set of feature. Our handwrtng mage s now appled for feature extracton whch helps n reducng the tranng complexty of neural networks and for reducng computaton tme before tranng process. In our analyss we use daubeches 5/3 nteger to nteger wavelet transform for feature extracton. Feature extracton s to get most relevant nformaton from orgnal data and represent that nformaton n low densty regon. After the handwrtng mage s transformed by nteger to nteger wavelet transform, the coeffcent cannot be used drectly to descrbe the characterstcs. The man advantage of usng daubeches 5/3 nteger to nteger wavelet transform s that, t s Fg.2. Trple level decomposton usng 5/3 ITI WT 5. NEURAL NETWORK Neural networks are usually traned for condtons to solve problems, whch are dffcult for conventonal computer and human bengs. After feature extracton the restored mage s compared wth our orgnal mage for wrter dentfcaton whch s traned by usng neural network usng back propagaton. Intally we have created 2 layer back propagaton network usng neural tool [4, 6]. 77

5 val fal mu gradent Mean Squared Error (mse) ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, FEBRUARY 24, VOLUME: 4, ISSUE: 3 Best Valdaton Performance s at epoch 8 Tran Valdaton Test Best 4.2 Fg.3. Created 2 Layer Neural Network The created 2 layer network requres nput and target mage for tranng the network, whch helps for the wrter dentfcaton. After the repeated tranng wth nput and dfferent target mage, wrter dentfcaton s done through the obtaned result from tranng process [7, 9]. Once the network weghts and bases are ntalzed, the network s ready for tranng. The multlayered feed forward network can be traned for functon approxmaton (nonlnear regresson) or pattern recognton s shown n the Fg.3. The tranng process requres a set of examples of proper network behavor network nputs p and target outputs t. The default performance functon for feed forward networks s the mean square error. The average squared error between the networks outputs a and the target outputs t s defned as follows: n F mse (2) N N 2 n 2 e t a Network tranng functon that updates the weght and bas values s done accordng to Levenberg-Marquardt optmzaton algorthm. One teraton of ths algorthm can be wrtten as, X k+ = X k-ak g k (3) where, X k s a vector of current weghts and bases, g k s the current gradent, and αk s the learnng rate. Ths equaton s terated untl the network converges. 6. EXPERIMENTAL RESULTS The performance plot shows the value of the performance functon versus the teraton number. It plots the tranng, valdaton and test performances. The tranng state plot shows the progress of other tranng varables, such as the gradent magntude, the number of valdaton checks, etc. The regresson plot shows a regresson between network outputs and network targets and to valdate the network performance. The valdaton and test results also shows that the R values are greater than.9. The scatter plot s helpful n showng that certan data ponts have poor fts. The three axes represent the tranng, valdaton and testng data. The dashed lne n each axs represents the perfect result outputs = targets. The sold lne represents the best ft lnear regresson lne between outputs and targets. The R value s an ndcaton of the relatonshp between the outputs and targets. If R =, ths ndcates that there s an exact lnear relatonshp between outputs and targets. If R s close to zero, then there s no lnear relatonshp between outputs and targets Epochs Fg.4. Performance valdaton Gradent =.25946, at epoch 24 Mu =., at epoch 24 Valdaton Checks = 6, at epoch Epochs Fg.5. Mean Square Error & Gradent for Created Network Fg.6. Regresson plot for 2 layer Neural Network 77

6 P MATHIVANAN et al.: WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION Table.. Performance varaton n the output s explaned by the targets Output Image Tran(R) Valdaton (R) Testng (R) Rob lutes Subramanya Bharath Davd Cameron The output results for 5 dfferent nput handwrtng mages are analyzed and ts output values of R lsted n the Table. as shown above. 7. CONCLUSION The proposed neural network wrter dentfcaton system s to justfy our nput mage matches wth the wrter. It has been greatly supported by slantng removal whch helps n segmentaton and feature extracton. Ths wrter dentfcaton system uses smplest and effcent watershed transform to segment handwrtten document whch makes ths system for a lossless feature extracton process. At the same tme t also faces some of the problems lke under and over segmentaton whch must be carefully handled n our segmentaton process by employng openng-by-reconstructon" and "closng-byreconstructon and n 2 layer neural network each tme we have to gve nput to the network f our nput mage sze s too large. REFERENCES [] S.K. Jayanth and D. Rajalakshm, Wrter dentfcaton for offlne Taml handwrtng based on gray-level cooccurrence matrces, Thrd Internatonal Conference on Advanced Computng, pp , 2. [2] C.M. Traveso, J.B. Alonso, O. Santana and M.A. Ferrer, Wrter dentfcaton based on graphology technques, IEEE Aerospace and Electronc Systems Magazne, Vol. 25, No. 6, pp , 2. [3] Yal Yang and Shunhua Shen, A handwrtng mage texture characterstcs extracton algorthm based on nteger-to-nteger wavelet transform, Thrd Internatonal Congress on Image and Sgnal Processng, Vol. 5, pp , 2. [4] K. Ubul, D. Tursun, A. Hamdulla and A. Aysa, A Feature Selecton and Extracton Method for Uyghur Handwrtng- Based Wrter dentfcaton, Internatonal Conference on Computatonal Intellgence and Natural Computng, Vol. 2, pp , 29. [5] Be-Be Zhu, Zhao-We Shang, Feng Zhang and Bo Yuan, Chnese handwrtng-based wrter dentfcaton wth PDTDFB transform, Internatonal Conference on Wavelet Analyss and Pattern Recognton, pp. 25-2, 29. [6] G. Loulouds, B. Gatos, I. Pratkaks and C. Halatss, Text lne and word segmentaton of handwrtten documents, Elsever Pattern recognton, Vol. 42, No. 2, pp , 29. [7] Y L, Yefeng Zheng, D. Doermann and S. Jaeger, Scrpt- Independent Text Lne Segmentaton n Freestyle Handwrtten Documents, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 3, No. 8, pp , 28. [8] M. Bulacu and L. Schomaker, Text-Independent Wrter Identfcaton and Verfcaton Usng Textural and Allographc Features, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 29, No. 4, pp. 7-77, 27. [9] V. Snghal, N. Navn and D. Ghosh, Scrpt-based classfcaton of hand-wrtten text documents n a multlngual envronment, Proceedngs of Thrteenth Internatonal Workshop on Research Issues n Data Engneerng: Mult-lngual Informaton Management, pp , 23. [] M.D. Adams and R.K. Ward, Symmetrc-extensoncompatble reversble nteger-to-nteger wavelet transforms, IEEE Transactons on Sgnal Processng, Vol. 5, No., pp , 23. [] L. Schomaker and M. Bulacu, Automatc wrter dentfcaton usng connected-component contours and edge-based features of uppercase Western scrpt, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 26, No. 6, pp , 24. [2] E. Kavalleratou, N. Fakotaks and G. Kokknaks, Slant estmaton algorthm for OCR system, Pattern Recognton, Vol. 34, No. 2, pp , 2. [3] H.E.S. Sad, T.N. Tan and K.D. Baker, Wrter Identfcaton Based on Handwrtng, Proceedngs of IEE Thrd European workshop on Handwrtng Analyss and Recognton, Vol. 33, No., pp , 2. [4] H.J. Km and C.C. L, Lossless and lossy mage compresson usng borthogonal wavelet transforms wth multplerless operatons, IEEE Transactons on Crcut and Systems-II: Analog and Dgtal Sgnal Processng, Vol. 45, No. 8, pp. 3-8, 998. [5] R.M. Boznovc and S.N. Srhar, Off-lne Cursve Scrpt Word Recognton, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol., No., pp , 989. [6] M. Yasuhara, Identfcaton and decomposton of fast handwrtng system, IEEE Transactons on Crcuts and Systems, Vol. 3, No., pp , 983. [7] M.D. Adams, Reversble nteger to nteger wavelet transforms for mage codng, PhD thess submtted to the Department of electrcal and computer engneerng, Unversty of Brtsh Columba, Vancouver, BC, Canada, 22. [8] E. Kavalleratou, N. Fakotaks and G. Kokknaks, New algorthms for skewng correcton and slant removal on word-level [OCR], Proceedngs of the Sxth IEEE Internatonal Conference on Electroncs, Crcuts and Systems, Vol. 2, pp ,