A Dataset of Online Handwritten Assamese Characters

Transcription

1 J Inf Process Syst, Vol.11, No.3, pp.325~341, September ISSN X (Prnt) ISSN X (Electronc) A Dataset of Onlne Handwrtten Assamese Characters Udayan Baruah* and Shyamanta M. Hazarka** Abstract Ths paper descrbes the Tezpur Unversty dataset of onlne handwrtten Assamese characters. The onlne data acquston process nvolves the capturng of data as the text s wrtten on a dgtzer wth an electronc pen. A sensor pcks up the pen-tp movements, as well as pen-up/pen-down swtchng. The dataset contans 8,235 solated onlne handwrtten Assamese characters. Prelmnary results on the classfcaton of onlne handwrtten Assamese characters usng the above dataset are presented n ths paper. The use of the support vector machne classfer and the classfcaton accuracy for three dfferent feature vectors are explored n our research. Keywords Assamese, Character Recognton, Dataset Collecton, Data Verfcaton, Onlne Handwrtng, Support Vector Machne 1. Introducton Onlne handwrtng recognton s a major research topc because of emergng technologes, such as PDAs and tablet PCs. Onlne recognton refers to the methods and technques dealng wth the automatc processng of a character as t s wrtten usng a dgtzer [1]. Development of any recognton system requres a standard dataset, whch s used for the tranng and testng of the system. The acquston and dstrbuton of standard datasets have ncreasngly ganed mportance. Some examples of datasets n the onlne doman are the Englsh dataset Pen-Based Recognton of Handwrtten Dgts [2], the French dataset IRONOFF [3], the Spansh dataset UJIpenchars [4], and the UNIPEN dataset [5]. All of whch contan onlne handwrtng data from varous alphabets (ncludng Latn and Chnese), sgnatures, and pen gestures. Research on onlne handwrtng recognton has also emerged for Indan scrpts. Examples of a few Indan scrpts where onlne handwrtng recognton actvtes are beng carred out nclude Taml [6], Telugu [7], Bengal [8], Devanagar [9], and Gurmukh [10]. Few Indan scrpts have also reported standard datasets of onlne handwrtten characters. But not all datasets of Indan scrpts are known to be publcly avalable. Some examples of few datasets of onlne handwrtngs n the context of Indan scrpts are, the HP Lab Taml Dataset [11], the Bangla Numeral Dataset [12] and the Devanagar Character Dataset [13]. Assamese s a major scrpt n the northeastern part of Inda. No onlne Ths s an Open Access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Non-Commercal Lcense ( whch permts unrestrcted non-commercal use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Manuscrpt receved July 08, 2013; frst revson December 18, 2013; second revson March 13, 2014; accepted Aprl 16, 2014; onlnefrst October 20, Correspondng Author: Udayan Baruah (udayan.baruah10@gmal.com) * Dept. of Informaton Technology, Skkm Manpal Insttute of Technology, Skkm , Inda (udayan.baruah10@gmal.com) ** Dept. of Computer Scence and Engneerng, Tezpur Unversty, Assam , Inda (shyamanta@eee.org) Copyrght 2015 KIPS

2 A Dataset of Onlne Handwrtten Assamese Characters handwrtten Assamese characters dataset s avalable n any standard onlne repostory of datasets. Therefore, the major objectve s to buld a dataset of onlne handwrtten Assamese characters. The characterstcs of Assamese characters (ncludng some dstnct propertes unque to t) are lsted below. a. Assamese wrtng has evolved from the ancent Indan scrpt, Brahm. Brahm had varous classes, and Assamese orgnated from the Gupta Brahm scrpt [14]. b. In Assamese there are 10 numerc characters and 52 basc alphabetc characters that consst of 11 vowels and 41 consonants. c. An Assamese character set s unque n that s has a large number of conjunct consonants (Juktakkhor) of about [14]. d. Certan vowels, consonants, and conjunct consonants have headlnes called matra n Assamese. e. There s no upper and lower case wrtng n Assamese. Ths makes Assamese characters dstnct from Englsh characters. In ths paper, we report on the development of a dataset of onlne handwrtten Assamese characters. The onlne handwrtten Assamese characters dataset reported n ths paper contans a total of 8,235 onlne handwrtten Assamese characters from 183 classes, whch consst of Assamese numerals, basc alphabetc characters, and conjunct consonants. We also present the prelmnary results on the classfcaton of onlne handwrtten Assamese characters. The paper s organzed as follows: Secton 2 descrbes the acquston of data, whch ncludes dscussons on an expermental set-up for data acquston (ncludng the data acquston program and data acquston protocol) and the nspecton of collected data for errors. Secton 3 provdes a dscusson on dfferent attrbutes of the collected data. The expermental results of character recognton on the samples of collected data are presented n Secton 4 and we present our fnal comments n Secton Data Acquston Data acquston s the collecton of onlne handwrtten samples from dfferent wrters. Ths nvolves the recordng of samples on some handwrtng nput devces lke PDAs, tablet PCs, and pen-tablets. These devces record the trajectory of the wrtng. 2.1 Expermental Setup for Data Acquston For the creaton of an onlne handwrtten Assamese characters dataset, onlne handwrtten samples of 121 conjunct consonants, along wth the 10 numerc characters and 52 basc alphabetc characters for Assamese [15], were collected. The total number of samples correspondng to each wrter s 183 (= 52 basc alphabetc characters + 10 numerals conjunct consonants). Thus there are a total of 8,235 samples n the dataset wrtten by 45 wrters (= ). Prnted Assamese Numerals, Basc Alphabetc Characters (Vowels and Consonants), and a few nstances of Conjunct Consonants (Juktakkhor) are shown n Table 1. Fgs. 1 3 show samples of onlne handwrtten Assamese characters (the mages of all the Assamese characters gven as reference shapes can also be downloaded along wth the dataset from The mages of the Assamese characters n prnted form are documented n the fle Data_Table.pdf n the dataset). The onlne Assamese handwrtng samples were collected on an -ball 8060U pen-tablet that was 326 J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

3 Udayan Baruah and Shyamanta M. Hazarka connected to a laptop and ts cordless dgtal stylus pen was used through a GUI. A screenshot of the GUI s shown n Fg. 4. The resoluton of the pen-tablet was 2540 LPI. The process of data acquston was guded by an expermental protocol. The expermental protocol descrbes the systematc steps for the collecton of data, whch mnmze varatons n terms of the place of wrtng of characters, wrtng envronment, and wrtng postures. The samples of the dataset were collected from students, research scholars, and faculty members of Tezpur Unversty. The wrters were 45 volunteers belongng to dfferent age groups rangng from years old wth the average age beng that of 31. Table 1. Prnted assamese characters Assamese numerals Assamese vowels a আ ঈ u ঊ ঋ e ঐ o ঔ Assamese consonants ক খ গ ঘ ঙ চ ছ জ ঝ ঞ ট ঠ ড ঢ ণ ত থ দ ধ ন প ফ ব ভ ম য ৰ ল ৱ শ ষ স হ k ড় ঢ় য় A few nstances of assamese conjunct consonants (Juktakkhor) k k k k k m p s s n t l h s n s b m n Data acquston program The GUI n the data acquston program shows a text nput box on the screen along wth some other controls. The sze of the text nput box s 4,392 4,868 ponts, where the coordnates are nteger numbers rangng from 0 to 4,392 for X and 0 to 4,868 for Y, respectvely. The value of X goes left-torght and that of Y goes downwards, assumng that the orgn (0, 0) les on the left-top corner of the text nput box. The acquston program records the handwrtng as a stream of (X, Y) coordnate ponts usng the approprate pen poston sensor along wth the pen-up/pen-down swtchng. The pressure level values are not recorded. A sngle clck on an onscreen Save button saves the current content of the text nput box. Ths also clears the current content of the text nput box so that the wrter can wrte the next character n the box. The unsaved content of the text nput box can be cleared by usng an onscreen Reset button. The data collecton system s ntalzed to the recordng mode by usng an onscreen Start button. The recorded nformaton of each onlne handwrtten character s stored n a text fle. Each fle s named based on the par (M, N). The text fle M.N.txt represents the character wth the ID M wrtten by the wrter wth the ID N. For nstance, the fle txt represents the character wth the ID 132 wrtten by the wrter wth ID 10. The dstrbuton of the dataset conssts of 45 folders (one for each wrter) and a Data_Table.pdf fle. Ths fle contans nformaton about the character ID (ID), character name (Label) and the prnted shape of the character (Char). Each folder contans 183 text fles correspondng to the 183 characters wrtten by a sngle wrter Expermental protocol for data acquston The onlne Assamese handwrtng samples were collected n a laboratory envronment. As wrters J Inf Process Syst, Vol.11, No.3, pp.325~341, September

4 A Dataset of Onlne Handwrtten Assamese Characters may not be at ease wth the wrtng nterface and the electronc pen, they were nstructed to practce by wrtng several characters n the text nput box before the actual recordng of characters started. After a wrter became famlar wth the onlne hand wrtng tools and the envronment, hs/her actual data recordng process took place accordng to the stages M0 to M4. Each wrter contrbuted wth hs/her handwrtng samples n a sngle, unnterrupted sesson. M0: The wrter s made to st n a relaxed poston wth the data collecton hardware setup n from of hm/her. M1: The lst of all 183 canddate characters s vewed by the wrter. M2: The wrter ntalzes the data collecton system. The correspondng wrter s ID and the ID of the frst character n the lst are entered through the GUI, whch s followed by a clck on the onscreen Start button. M3: (1) For each of the 183 characters (n the ascendng order of the seral number of the characters n the lst provded) startng at the frst character, the followng two consecutve steps are performed by the wrter: a: The character s wrtten n the text nput box of the GUI. b: The content of the text nput box s saved by usng the onscreen Save button. (2) If a mstake or unsatsfactory wrtng of any of the k (for k = 1, 2, 3,,183) characters s notced mmedately after the character s wrtten, but before t s saved, the followng two consecutve steps are performed by the wrter: a: The content of the text nput box s cleared by usng the onscreen Reset button. b: The steps of M3(1) are followed correspondng to the kth character. M4: All of the collected 183 samples are stored n the correspondng data folder. 2.2 Varablty n Wrtng Styles The shape of a character vares from wrter to wrter. Dfferent wrters wrte the same character dfferently. Smlarly, the wrters tend to wrte at dfferent locatons n the text nput box. Therefore, the characters of the same class are of varable shapes, varable szes, and a varable range of (X, Y) coordnate values. Some collected samples wrtten by dfferent wrters are shown n Fg. 1. The varablty of wrtng patterns of the same character s shown n Fg. 2. Fg. 1. Samples from the dataset of onlne handwrtten Assamese characters wrtten by dfferent wrters. In ths fgure we present samples from eght dfferent wrters. Fg. 2. Varablty of patterns of the same character wrtten by three dfferent wrters. 328 J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

5 Udayan Baruah and Shyamanta M. Hazarka Fg. 3. A screenshot of a part of the Vsual Data Inspecton Program. Fg. 4. Data acquston GUI. 2.3 Vsual Data Inspecton The vsual nspecton of data ams at detectng human as well as system errors. Examples of these types of errors are skppng some character, wrongly recordng some character, overwrtng a character, etc. A separate program was developed for vewng the samples collected mmedately after the completon of a data acquston sesson (refer to Fg. 3). Wth the help of ths program, t s possble to verfy data vsually n the presence of the wrter mmedately after hs/her sesson of data acquston and to overwrte the erroneous character fles by rewrtng those characters followng the expermental protocol. 3. Data Attrbutes Each character n the dataset contans attrbute nformaton. These attrbutes are the Name, Number of Strokes, and Sequence of Strokes assocated wth the character. J Inf Process Syst, Vol.11, No.3, pp.325~341, September

6 A Dataset of Onlne Handwrtten Assamese Characters 3.1 Character Name The frst lne of each sample s CHARACTER_NAME: Character (as shown for a sample character n Fg. 9 n Appendx A). The Character s the Name of any one of the 183 characters. 3.2 Number of Strokes n a Sample A stroke s a sequence of ponts from the pen-down to the pen-up events. The total number of strokes used to wrte a character s represented by the lne STROKE_COUNT: Number refer to Fg. 9 n Appendx A), where Number s the total count of the strokes n the character. 3.3 Sequence of Strokes Each stroke begns wth the PEN_DOWN nformaton and the PEN_UP nformaton s followed by the PEN_DOWN nformaton between the two consecutve strokes. The end of a sample s represented by the PEN_UP nformaton, whch s followed by the END_CHARACTER: Character nformaton. Each stroke conssts of a sequence of X and Y coordnates values, whch are gven n the frst and the second columns of the text fle, respectvely. Correspondng to each par of values of X and Y coordnates, the STYLUS_STATE and STROKE nformaton s gven n the thrd and the fourth columns, of the text fle, respectvely (refer to Fg. 9 n Appendx A). The STYLUS_STATE s ether 1 or 0. Correspondng to each recorded (X, Y) pont, the STYLUS_STATE s 1 and correspondng to the PEN_UP nformaton the STYLUS_STATE s 0. STYLUS_STATE s kept blank correspondng to each pece of PEN_DOWN nformaton. The STROKE nformaton represents the seral number of the consttuent stroke of a sample. 4. Character Recognton Character recognton experments were performed on the 10 numeral classes (a total of 45 10=450 numerals) and 52 classes of basc alphabetc characters (a total of 45 52=2340 basc alphabetc characters) avalable n the dataset. The archtecture for the classfcaton of characters s shown n Fg. 5. In order to conduct the classfcaton stage, we explored the support vector machne (SVM) [16] wth three dfferent kernels lnear, polynomal, and radal bass functon (RBF). Dataset of onlne handwrtten Assamese characters Preprocessng Sze Normalzaton Smoothenng Resamplng Preprocessed character Computaton of features Feature Set SVM classfer Classfcaton results Fg. 5. Archtecture for the classfcaton of characters. 330 J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

7 Udayan Baruah and Shyamanta M. Hazarka 4.1 Data Preprocessng Sze normalzaton Onlne handwrtten characters are sequences of two-dmensonal coordnate ponts (refer to Fg. 10 n Appendx A). Snce the characters are wrtten n dfferent szes at dfferent locatons of the text nput box, all of the characters have dfferent ranges of values for the X and Y coordnates. Therefore, the dfferent ranges of values for the coordnate ponts need to be normalzed to a unform range of values. Ths step s performed by normalzng the values of the X and Y coordnates of the characters to the range (0, 200). Fg. 6(a) shows the orgnal character and the character after sze normalzaton s shown n Fg. 6(b). (a) Fg. 6. (a) Orgnal character and (b) character after sze normalzaton. (b) Smoothng The smoothng of characters s requred to reduce the sharp changes or roughness (jtters) from the stroke. It removes jtters from the data whle preservng underlyng patterns. Here, the smoothng of the characters was performed usng a 3-pont movng average flter n order to remove jtters from the characters. Fg. 7(b) represents the smoothng result of the character shown n the Fg. 7(a). (a) Fg. 7. (a) Orgnal character and (b) character after smoothng. (b) Resamplng An onlne handwrtten character s a strng of coordnate ponts. Dfferent characters have a dfferent number of ponts along the trajectory from the start to the end. Therefore, dfferent characters need to be resampled to fxed number ponts. Here, all of the characters have been resampled to a fxed number of 100 ponts. Fg. 8(b) represents the resamplng of the character shown n the Fg. 8(a) to 100 ponts. (a) Fg. 8. (a) Orgnal character and (b) character after resamplng. (b) J Inf Process Syst, Vol.11, No.3, pp.325~341, September

8 A Dataset of Onlne Handwrtten Assamese Characters 4.2 Feature Sets We wll now present the classfcaton results, whch are based on the followng feature vectors that were computed from the characters taken from the dataset above. The feature set FS:2 s based on [17] and the feature set FS:3 s an enhancement of the feature set FS:2. The feature set FS:3 ncludes all the features of FS:2 along wth fve other features Feature set FS:1 X and Y coordnate values of the resampled characters. The values of the X and Y coordnates of the characters are dvded nto sxty-four zones or subntervals of the normalzed range of values (0, 200) of X and Y. The number of ponts n a character fallng nto each sub-nterval of (0, 200) s consdered as a feature of the character (zone feature). Coordnates of the start and end ponts of each character. Dstance between the start and the end ponts of the character. Number of strokes n the character. Mean of the X and Y coordnate values. Standard devaton of X and Y coordnates values. Headlne feature Feature set FS:2 Normalzed horzontal coordnates. Normalzed vertcal coordnates. Drecton angle wth reference to the horzontal axs (cosne of the angle). Drecton angle wth reference to the vertcal axs (sne of the angle). Curvature wth reference to the horzontal axs. Curvature wth reference to the vertcal axs. Poston of the stylus (up or down) Feature set FS:3 Normalzed horzontal coordnates. Normalzed vertcal coordnates. Drecton angle wth reference to the horzontal axs (cosne of the angle). Drecton angle wth reference to the vertcal axs (sne of the angle). Curvature wth reference to the horzontal axs. Curvature wth reference to the vertcal axs. Poston of the stylus (up or down). The number of ponts contaned n each of the sxty-four subntervals of the character (zone feature). Dstance between the start and the end ponts of the character. Number of strokes n the character. Mean of the X and Y coordnate values. Standard devaton of the X and Y coordnate values. 332 J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

9 Udayan Baruah and Shyamanta M. Hazarka 4.3 SVM Based Classfcaton Experments usng polynomal kernel and Gaussan radal bass functons, whch are the common choces for kernel functons n SVM, were performed for the classfcaton of both numerals and basc alphabetc characters. A k-fold cross valdaton procedure was used for the tranng and testng of the SVM classfer wth varous parametrc settngs. In k-fold cross-valdaton, the orgnal sample was randomly parttoned nto k equal sze subsamples. Of the k subsamples, a sngle subsample was retaned as the valdaton data for testng the model, and the remanng k 1 subsamples was used as tranng data. The cross-valdaton process was then repeated k tmes, n whch each of the k subsamples were used exactly once as the valdaton data. The k results from the folds were then combned to produce a sngle estmaton. All of the observatons were used for both tranng and valdaton, and each observaton was used for valdaton exactly one tme. 10-fold cross-valdaton was done n ths work Support vector machnes SVMs (Support Vector Machnes) are a useful technque for data classfcaton [16]. The goal of SVM s to produce a model (based on the tranng data), whch predcts the target values of the test data gven only the test data attrbutes. n Gven a tranng set of nstance-label pars x, y ), 1,......, l where x R and y } ( the SVMs requre that a soluton s found for the followng optmzaton problem: l { 1, 1, mn w, b, subject to 1 2 T y ( w ( x ) b) 1, 0 T w w C l 1 x are mapped nto a hgher dmensonal space by the functon. SVM Here, the tranng vectors fnds a lnear separatng hyper plane wth the maxmal margn n ths hgher dmensonal space. C 0 T s the penalty parameter of the error term. Furthermore, K ( x, x j ) ( x ) ( x j ) s called the kernel functon. The three types of kernels that are used the most are as follows: T Lnear: K ( x, x j ) x x j T d Polynomal: K ( x, x j ) ( x x j r), 0 2 Radal Bass Functon (RBF): K ( x, x ) exp( x x ), 0 j Here,, r and d are the kernel parameters. In ths experment, we have used the SVM wth the lnear, polynomal, and RBF kernels. j 4.4 Expermental Results The overall recognton rates acheved for the onlne handwrtten assamese basc alphabetc characters were 71.54% (usng lnear kernel), 75.39% (usng polynomal kernel), and 78.38% (usng RBF kernel) wth a 10 fold cross valdaton process based on the feature set FS:1 (refer to Table 2). A total of 2,340 characters were used as samples n the basc alphabetc character recognton experment. The kernel J Inf Process Syst, Vol.11, No.3, pp.325~341, September

10 A Dataset of Onlne Handwrtten Assamese Characters Table 2. Statstcs for the recognton rates Type of SVM kernel Across classes Total no. Correctly of classfed Average SD of the nstances nstances recognton recognton Average±SD rate (%) rate (%) Onlne handwrtten Assamese basc alphabetc characters Feature set FS:1 Lnear (C=1, E=1) ±15.77 Polynomal (C=3, E=4) ±14.39 RBF (C=9, Gamma=0.07) ±13.31 Feature set FS:2 Lnear (C=1, E=1) ±14.29 Polynomal (C=3, E=4) ±12.23 RBF (C=8, Gamma= ) ±11.50 Feature set FS:3 Lnear (C=1, E=1) ±13.45 Polynomal (C=3, E=4) ±12.24 RBF (C=8, Gamma= ) ±11.08 Onlne handwrtten Assamese numerals Feature set FS:1 Lnear (C=1, E=1) ±2.21 Polynomal (C=1, E=4) ±1.54 RBF(C=3, Gamma=0.05) ±1.16 Feature set FS:2 Lnear (C=1, E=1) ±5.37 Polynomal (C=1, E=4) ±2.21 RBF (C=8, Gamma= ) ±1.79 Feature set FS:3 Lnear (C=1, E=1) ±3.32 Polynomal (C=1, E=4) ±2.56 RBF (C=8, Gamma= ) ±1.79 Table 3. Recognton rates of onlne handwrtten Assamese basc alphabetc characters based on FS:1 for dfferent combnatons of the polynomal kernel parameters C and E C (complexty parameter) E (exponent) Recognton rate (%) J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

11 Udayan Baruah and Shyamanta M. Hazarka parameter settng C = 3 and E = 4, whch are assocated wth the polynomal kernel, was obtaned by varyng dfferent values of C and E wthn a range and gettng the recognton rate at each combnaton of parameters. Here C s the complexty (or penalty) parameter and E s the exponent of the polynomal kernel. The recognton rates of the basc alphabetc characters based on FS:1 for dfferent combnatons of the parameters C and E assocated wth the polynomal kernel are shown n Table 3. Smlarly, the kernel parameter settng C = 9 and gamma = 0.07 assocated wth RBF kernel was obtaned by varyng dfferent values of C and gamma wthn a range and gettng the recognton rate at each combnaton of parameters. Smlarly, based on FS:2 [17], the overall recognton rate acheved was 80.29% and based on FS:3, the overall recognton rate acheved was 81.15% (refer to Table 2). The ndvdual recognton rates (n percentage) of the basc alphabetc characters class are represented n Table 4. Table 4. Indvdual recognton rates of the onlne handwrtten Assamese basc alphabetc characters Sl. no. Class Feature set FS:1 RBF kernel wth C=9 & Gamma= 0.07 Recognton rate (%) Feature set FS:2 RBF kernel wth C=8 & Gamma= Feature set FS:3 RBF kernel wth C=8 & Gamma= Sl. no. Class Feature set FS:1 RBF kernel wth C=9 & Gamma= 0.07 Recognton rate (%) Feature set FS:2 RBF kernel wth C=8 & Gamma= Feature set FS:3 RBF kernel wth C=8 & Gamma= A TA AA THA E DA EE DHA U NA UU PA REE PHA AE BA OI BHA O MA OU AJA KA RA KHA LA GA WA GHA TXA NG MXA CA DXA CCA HA JA KHYA JHA AYA NIYA DRA MTA DHRA MTHA KTA MDA ANSR MDHA BXG MNA CBN The overall recognton rate acheved for the onlne handwrtten Assamese numerals was 99.11% (refer to Table 2), whch was obtaned by usng the polynomal kernel wth the kernel parameter settngs C = 1 and E = 4 and a 10 fold cross valdaton process based on FS: Assamese numerc J Inf Process Syst, Vol.11, No.3, pp.325~341, September

12 A Dataset of Onlne Handwrtten Assamese Characters characters were used as samples n the experment of numeral recognton. The parameter settng C = 1 and E = 4 was obtaned by varyng dfferent values of C and E wthn a range and gettng the recognton rate at each combnaton of parameters. Smlarly, based on the feature set FS:2 [17], the overall recognton rate acheved was 98.00% (refer to Table 2) and based on FS:3, the overall recognton acheved was 97.78% (refer to Table 2). The ndvdual recognton rates (n percentage) of the numeral classes are represented n Table 5. Table 5. Indvdual recognton rates of onlne handwrtten Assamese numerals Recognton rate (%) Sl. Class Feature set FS:1 Feature set FS:2 Feature set FS:3 No. Polynomal kernel wth Polynomal kernel wth RBF kernel wth C=1 & E=4 C=1 & E=4 C=8 & Gamma= SUNYA EK DUI TINI CARI PAC CAY XAT ATH NAA Shape Analyss of Smlar Characters The overall recognton rates acheved for the onlne handwrtten Assamese basc alphabetc characters s not very encouragng. Ths may be because the major strokes of several characters are almost smlar to each other. Accordngly, there are chances of these characters beng msclassfed, whch would result n a low recognton rate. Table 6 llustrates some of these characters and each row n Table 6 represents smlar characters from dfferent classes. Table 6. Smlar characters from dfferent classes Smlar characters GHA AJA BHA MDA MA THA CA MDHA MNA PA 336 J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

13 Udayan Baruah and Shyamanta M. Hazarka Improvement n the recognton rates (refer to Table 4) of some of these characters has been notced (from feature set FS:1 through feature set FS:3) when the drecton angle feature and curvature feature along wth zone feature are used. Identfyng the dscrmnatng features for an mproved classfcaton of these types of smlar characters s part of ongong research. 5. Fnal Comments In ths work, we have reported on the development of a dataset of onlne handwrtten Assamese characters. The samples were collected from a varety of wrters belongng to varous groups, n order to acheve a varety of wrtng patterns of characters. The samples of the dataset were not preprocessed. The recorded nformaton was drectly stored n raw format, whch provded a scope for applyng dfferent preprocessng technques to the dataset. The dataset of onlne handwrtten Assamese characters can be downloaded for free from the UCI Machne Learnng Repostory [18]. Ths reported dataset s the only publcly avalable dataset of onlne handwrtten Assamese characters. The dataset ams at provdng samples for research n onlne handwrtng recognton for Assamese scrpts. The prelmnary results on the recognton of numerals and basc alphabetc characters taken from the dataset are reported n ths work. From among the three SVM kernels used n ths experment on numerals, the polynomal kernel gves the best recognton rate of 99.11% based on the feature set FS:1. The recognton rate obtaned n the case of onlne handwrtten Assamese numerals s hgher than the recognton rate of 96.6% obtaned n an HMM based technque reported n [19]. From among the three SVM kernels used n ths experment for Assamese alphabetc characters, the RBF kernel gves the best recognton rate of 81.15% based on the feature set FS:3. No results are avalable n the lterature on the topc of recognton of onlne handwrtten Assamese alphabetc characters. Explorng a robust set of features for mprovng the recognton of characters and extendng the same for recognton of conjunct consonants (Juktakkhor) s part of ongong research. Acknowledgement The Onlne Handwrtten Assamese Characters Dataset s the result of onlne handwrtng samples contrbuted by students, research scholars, and faculty members of Tezpur Unversty. Ther support s gratefully acknowledged. References [1] R. Plamondon and S. Srhar, Onlne and offlne handwrtng recognton: a comprehensve survey, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 22, no. 1, pp , [2] E. Alpaydn and Fevz. Almoglu, Pen-based recognton of handwrtten dgts dataset, 1998; edu/ml/datasets/pen-based+recognton+of+handwrtten+dgts. [3] C. Vvard-Gaurdn, P. M. Lallcan, S. Knerr, and P. Bnter, IRESTE On/Off (IRONOFF) dual handwrtng database, n Proceedng of the 5th Internatonal Conference on Document Analyss and Recognton, Bangalore, Inda, 1999, pp J Inf Process Syst, Vol.11, No.3, pp.325~341, September

14 A Dataset of Onlne Handwrtten Assamese Characters [4] D. Llorens, F. Prat, A. Marzal, J. M. Vlar, M. J. Castro, J. C. Amengual, S. Barrachna, A. Castellanos, S. España, J. A. Gómez, J. Gorbe, A. Gordo, V. Palazón, G. Pers, R. Ramos-Garjo, and F. Zamora, The UJIpenchars Database: a pen-based database of solated handwrtten characters, n Proceedng of the 6th Internatonal Conference on Language Resources and Evaluaton (LREC), Marrakech, Morocco, 2008, pp [5] I. Guyon, L. Schomaker, R. Plamondon, M. Lberman, and S. Janet, UNIPEN project of on-lne data exchange and recognzer benchmarks, n Proceedng of the 12th IAPR Internatonal Conference on Pattern Recognton, Jerusalem, Israel, 1994, pp [6] A. Bharath and S. Madhvanath, Hdden Markov model for onlne handwrtten Taml word recognton, n Proceedngs of the 9th Internatonal Conference on Document Analyss and Recognton, Curtba, Brazl, 2007, pp [7] L. Prasanth, J. Babu, R. Sharma, and P. Rao, Elastc matchng of onlne handwrtten Taml and Telegu scrpts usng local features, n Proceedngs of the 9th Internatonal Conference on Document Analyss and Recognton, Curtba, Brazl, 2007, pp [8] U. Bhattacharya, B. K. Gupta, and S. K. Paru, Drecton code based features for recognton of onlne handwrtten characters of Bangla, n Proceedngs of the 9th Internatonal Conference on Document Analyss and Recognton, Curtba, Brazl, 2007, pp [9] N. Josh, G. Sta, A. G. Ramakrshnan, V. Deepu, and S. Madhvanath, Machne Recognton of Onlne Handwrtten Devanagar Characters, n Proceedngs of the 8th Internatonal Conference on Document Analyss and Recognton, Seoul, Korea, 2005, pp [10] A. Sharma, R. Kumar, and R. K. Sharma, Onlne handwrtten Gurmukh character recognton usng elastc matchng, n Proceedngs of the Congress on Image and Sgnal Processng, Hanan, Chna, 2008, pp [11] A. S. Bhaskarabhatla and S. Madhvanath, Experences n collecton of handwrtng data for onlne handwrtng recognton n Indc scrpts, n Proceedngs of the 4th Internatonal Conference on Language Resources and Evaluaton, Lsbon, Portugal, [12] B. B. Chaudhur, A complete handwrtten numeral database of Bangla: a major Indc scrpt, n Proceedngs of the 10th Internatonal Workshop on Fronters n Handwrtng Recognton, France, [13] K. C. Santosh, C. Nattee, and Bart Lamroy, Relatve postonng of stroke based clusterng: a new approach to on-lne handwrtten Devanagar character recognton, Internatonal Journal of Image & Graphcs (IJIG), vol. 12, no. 2, [14] N. Sahara and K. M. Konwar, LutPad: a fully uncode compatble Assamese wrtng software, n, Proceedngs of the 2nd Workshop on Advances n Text Input Methods (WTIM 2), Mumba, Inda, 2012, pp [15] N. S. Bhabendra, Amar Akhar (Second Part). Guwahat: Assam Book Hve, [16] C. W. Hsu, C. C. Chang, and C. J. Ln, A practcal gude to support vector classfcaton, Department of Computer Scence and Informaton Engneerng, Natonal Tawan Unversty, Tape, Tawan, [17] A. R. Ahmed, C. V. Gaudn, M. Khald, and R. Yusof, Onlne handwrtng recognton usng support vector machne, n Proceedngs of the 2nd Internatonal Conference on Artfcal Intellgence n Engneerng and Technology, 2004, Sabah, Malaysa, pp [18] U. Baruah and S. M. Hazarka, Onlne handwrtten Assamese characters dataset, 2011; datasets/onlne+handwrtten+assamese+characters+dataset. [19] G. S. Reddy, P Sharma, S. R. M. Prasanna, C. Mahanta and L. N. Sharma, Combned onlne and offlne Assamese handwrtten numeral recognzer, n Proceedngs of the Natonal Conference on Communcatons (NCC2012), Kharagpur, 2012, pp J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

15 Udayan Baruah and Shyamanta M. Hazarka Appendx A An llustraton on representaton of handwrtten characters n the dataset s gven below. The text fle correspondng to ths data format s txt, whch represents the sample wth name TTT, Character ID 77 wrtten by the wrter wth Wrter ID 37. The.TXT fle representaton of the character TTT correspondng to Wrter ID 37 s reproduced n Fg. 9. A plot of the (X, Y) ponts of the correspondng character s shown n the Fg. 10. From the character lstng Data_Table.pdf, t can be verfed that the name of the character s TTT. CHARACTER_NAME: TTT STROKE_COUNT: 2 X Y STYLUS_STATE STROKE PEN_DOWN PEN_UP 0 PEN_DOWN J Inf Process Syst, Vol.11, No.3, pp.325~341, September

16 A Dataset of Onlne Handwrtten Assamese Characters PEN_UP 0 END_CHARACTER: TTT Fg. 9. The.TXT fle representaton of the character TTT correspondng to Wrter ID 37. Fg. 10. Plot of the character TTT correspondng to Wrter ID J Inf Process Syst, Vol.11, No.3, pp.325~341, September 2015

17 Udayan Baruah and Shyamanta M. Hazarka Udayan Baruah He s an Assocate Professor of Informaton Technology at Skkm Manpal Insttute of Technology, Skkm, Inda. He receved hs M.Sc. n Mathematcs from the Faculty of Mathematcal Scences, North Campus, Unversty of Delh, Inda and M.Tech. n Informaton Technology from the Department of Computer Scence and Engneerng, Tezpur Unversty, Assam, Inda. Presently, he s workng towards hs Ph.D. degree n the Department of Computer Scence and Engneerng, Tezpur Unversty. Hs feld of research s Onlne Handwrtng Recognton. Shyamanta M. Hazarka He s a Professor of Computer Scence and Engneerng at Tezpur Unversty, Assam, Inda. He completed hs B.E. from Assam Engneerng College, Guwahat, Inda, M.Tech. n Robotcs from IIT Kanpur, Inda and Ph.D. from Unversty of Leeds, England. He has been a Full Professor for Cogntve Systems and Neuro Informatcs at the Cogntve Systems Group, Unversty of Bremen, Germany, for wnter Hs work has focused prmarly n knowledge representaton and reasonng and rehabltaton robotcs wth an am towards development of ntellgent assstve systems. J Inf Process Syst, Vol.11, No.3, pp.325~341, September