PERFORMANCE EVALUATION OF QUANTITATIVE METRICS ON ANCIENT TEXT DOCUMENTS USING MIGT

Size: px

Start display at page:

Download "PERFORMANCE EVALUATION OF QUANTITATIVE METRICS ON ANCIENT TEXT DOCUMENTS USING MIGT"

Gervais Walker
5 years ago
Views:

1 PERFORMANCE EVALUATION OF QUANTITATIVE METRICS ON ANCIENT TEXT DOCUMENTS USING MIGT N. VENKATA RAO, Dr. A.S.C.S.SASTRY, Dr. A.S.N.CHAKRAVARTHY, A.V.SRINIVASA RAO Rsarch Scholar, Dpartmnt of ECE, K.L.Univrsity, Vaddswaram, Andhra Pradsh, India Profssor, Dpartmnt of ECE, K.L.Univrsity, Vaddswaram, Andhra Pradsh, India Assoc. Profssor, Dpartmnt of CSE, Univrsity Collg of Enginring, Kakinadda, Andhra Pradsh, India Assistant Profssor, Dpartmnt of ECE, Sasi Institut of Tchnology and Engg, Tadpalligudm, Andhra Pradsh, India ABSTRACT In th prsnt world scnario Optical Charactr Rcognition (OCR) has wid varity of applications in th txt documnt imag analysis for rcognizing individual charactrs of any languag. Digitizing th old documnts is a tough job for prsrving th ssnc of th documnts to th coming ras. In this papr w ar summarizing diffrnt imag quantitativ mtrics for stimating th loss of information from th imag aftr claning th noisy imag by using anyon of th local or non-local thrsholding tchniqus. Th quality valuations ar mad on 0 Tlugu and English txt documnts aftr claning th documnts with Modifis Itrativ Global Thrshold (MIGT) approach. Kywords: Thrsholding Tchniqus, Digitization, Evaluation, Optical Charactr Rcognition, Documnt Imag Analysis. INTRODUCTION In imag procssing, th gray lvls of pixls blonging to th objct ar substantially diffrnt from th gray lvls of th pixls blonging to th background. This kind of action w will obsrv in many applications of Imag Procssing. Thrsholding is a tchniqu, which is a simpl and ffctiv tool to sparat objcts from th background. Som of th xampls of thrsholding applications ar documnt imag analysis whr th printd charactrs ar xtractd [,] xtraction of th charactrs from th ancint documnts [], graphical contnt analysis, musical scors: map procssing, scn procssing, tc. Th rsult of th thrsholding opration is a binary imag whos on stat will indicat th forground objcts, that is, printd txt, a lgnd, a targt, dfctiv part of a matrial, tc., whil th complmntary stat will corrspond to th background. Basd on th application, th forground can b rprsntd by gray-lvl 0, that is, black as for txt, and th background by th highst luminanc for documnt papr, that is in 8-bit imags, or convrsly th forground by whit and th background by black. Various factors, influnc th smooth opration of thrsholding lik corrlatd nois, high dnsity of pixls in a givn ara, ovrlapping of pixl information, blurrinss of an imag all ths will complicat th thrsholding opration. Finally, th lack of objctiv masurs to assss th prformanc of various thrsholding algorithms, and th difficulty of xtnsiv tsting in a task orintd nvironmnt, ar th othr major handicaps. Mhmt Szgin and Bulnt Sankur [] prformd a survy on imag thrsholding tchniqus and quantitativ prformanc valuation on diffrnt imags. Yang Gao-bo t.al. proposd [] an objctiv prformanc valuation on vido sgmntation algorithms with ground truth imags. K.Ntirogiannis, t. al. proposd [] an valuator mthodology for Documnt Imag Binarization tchniqus using ground truth imags. Chun Ch Fung and Rapporn Chamchony proposd [8] a rviw of valuation on Optical Binarization tchniqus for charactr sgmntation 0

2 in historical manuscripts. Štpán Šrubar statd in postr prsntation [] that th Sgmntation valuation mthods hav variabl quality. Som of thm ar focusd on a small part of information from th whol sgmntation and th quality is thrfor poor. Evn som rcntly proposd mthods do not assur high quality. Th bst rsults wr providd by mthod which uss grouping of sgmnts and distanc masuring. This quality masurmnt is on of th biggst according to th siz of tst st as wll as th numbr of mthods which can nsur high lvl of objctivity. Ruchika Sharma t.al. prsnt [0] comparision of binarization tchniqus using svral obsrvations, tchniqus and mthods. Ths approachs aiming at rmoval of background nois from camra-basd imags. V. Rabux t.al. prsntd [] 8 faturs that charactriz th quality of a documnt imag. Ths faturs ar usd in stp-wis multivariat linar rgrssion to crat prdiction modls for binarization mthods. Rpatd random subsampling cross-validation shows that th modls ar accurat (max prcntag rror quals %). Morovr, givn th stp-wis approach of th linar rgrssion, ths modls ar not ovr fit. As a rsult, 0 modls out of ar validatd and show sufficint accuracy to b usd in an automatd slction mthod of th optimal binarization mthod for ach imag. In this papr w dscribd diffrnt quantitativ tchniqus which ar Misclassification rror, Rgion non-uniformity, Rlativ forground ara rror, Hausdorff distanc and Jaccard distanc for stimating th loss of information during th claning procss of ancint Tlugu and English txt sampls. Bfor stimating th quantitativ mtrics, th documnts ar cland by Itrativ Global Thrshold algorithm. This papr is organizd in to four sctions. In th first sction introduction, litratur survy and problm dfinition ar discussd. In th scond sction th quantitativ mtrics for stimating th prformanc ofmigt algorithm for th claning of ancint documnts is discussd. In th third sction, xprimntal rsults ar discussd. Th last sction givs th conclusions and futur scop of work.. METHODOLOGY Claning of Noisy Documnts Binarization is on of th svral stps usd in most documnt imag analysis systms. It consists of labling ach pixl in an imag as forground and background. It provids a propr distinction btwn background and forground. In this papr w usd Modifid Itrativ Global Thrshold algorithm for claning th documnts which is proposd [] by us. Thrsholding prformanc critria Automatd imag thrsholding ncountrs difficultis whn th forground objct constituts a disproportionatly small larg ara of th scn, or whn th objct and background gray lvls possss substantially ovrlapping distributions, vn rsulting in a unimodal distribution. Furthrmor, th histogram can b noisy if its stimat is basd on only a small sampl siz, or it may hav a comb-lik structur du to histogram strtching/qualization fforts. Consquntly, misclassifid pixls and shap dformations of th objct may advrsly affct th quality-tsting task in Non-Dstructiv Tsting(NDT) applications. On th othr hand, thrsholdd documnt imags may nd up with nois pixls both in th background and forground, spoiling th original charactr bitmaps. Thrsholding may also caus charactr dformations such as chipping away of charactr stroks or convrsly adding bumps and mrging of charactrs among thmslvs and/or with background objcts. Spurious pixls as wll as shap dformations ar known to critically affct th charactr rcognition rat. Thrfor, th critria to assss thrsholding algorithms must tak into considration both th noisinss of th sgmntation map as wll as th shap dformation of th charactrs, spcially in th documnt procssing applications. To put into vidnc th diffring prformanc faturs of thrsholding mthods, w hav usd th following fiv prformanc critria: misclassification rror (ME), dg mismatch (EMM), rlativ forground ara rror (RAE), Modifid Hausdorff Distanc (MHD), and rgion nonuniformity (NU). Obviously, ths fiv critria ar not all indpndnt: for xampl, thr is a crtain amount of corrlation btwn misclassification rror and rlativ forground ara rror, and similarly, btwn dg mismatch and

3 Hausdorff distanc, both of which pnaliz shap dformation. Th rgion nonuniformity critrion is not basd on ground truth data, but judgs th intrinsic quality of th sgmntd aras. W hav adjustd ths prformanc masurs so that thir scors vary from 0 for a totally corrct sgmntation to for a totally rronous cas. Misclassification rror Misclassification rror (ME) rflcts th prcntag of background pixls wrongly assignd to forground, and convrsly, forground pixls wrongly assignd to background. For th two-class sgmntation problm, ME can b simply xprssd as: whr A 0 is th ara of rfrnc imag, and A T is th ara of thrsholdd imag. Obviously, for a prfct match of th sgmntd rgions, RAE is zro, whil if thr is zro ovrlap of th objct aras, th pnalty is th maximum on. Hausdorff distanc Th Hausdorff distanc can b usd to assss th shap similarity of th thrsholdd rgions to th ground-truth shaps. Rcall that, givn two finit sts of points, say ground-truth and thrsholdd forground rgions, thir Hausdorff distanc is dfind as H(FO,FT) = max{dh(fo,ft), dh(ft,fo)} whr B 0 and F 0 dnot th background and forground of th original (ground-truth) imag, BT and FT dnot th background and forground ara pixls in th tst imag, and. is th cardinality of th st. Th ME varis from 0 for a prfctly classifid imag to for a totally wrongly binarizd imag. Rgion nonuniformity This masur, dos not rquir groundtruth information, is dpnds on varianc of th whol imag is dfind as Whr dh(fo,ft) = max d(fo, FT) fo ε FO = max min fo-ft, fo ε FOfT ε FT and fo ft dnots th Euclidan distanc of two pixls in th ground-truth and thrsholdd objcts. Sinc th maximum distanc is snsitiv to outlirs, w hav masurd th shap distortion via th avrag of th modifid Hausdorff distancs (MHD) ovr all objcts. Th modifid distanc is dfind as: whr σ rprsnts th varianc of th whol imag, and σ f rprsnts th forground varianc. It is xpctd that a wll-sgmntd imag will hav a nonuniformity masur clos to 0, whil th worst cas of NU= corrsponds to an imag for which background and forground ar indistinguishabl up to scond ordr momnts. Rlativ forground ara rror Th comparison of objct proprtis such as ara and shap, as obtaind from th sgmntd imag with rspct to th rfrnc imag, has bn usd in Zhang undr th nam of rlativ ultimat masurmnt accuracy (RUMA) to rflct th fatur masurmnt accuracy. W modifid this masur for th ara fatur A as follows: For xampl, th MHDs ar calculatd for ach x pixl charactr box, and thn th MHDs ar avragd ovr all charactrs in a documnt. Notic that, sinc an uppr bound for th Hausdorff distanc cannot b stablishd, th MHD mtric could not b normalizd to th intrval [0, ], and hnc is tratd sparatly (by dividing ach MHD valu to th highst MHD valu ovr th tst imag st NMHD). Jaccard distanc Th Jaccard distanc, which masurs dissimilarity btwn sampl sts, is complmntary to th Jaccard cofficint and is obtaind by subtracting th Jaccard cofficint from, or, quivalntly, by dividing th diffrnc of th sizs of th union and th intrsction of two sts by th siz of th union:

documnt by using any local/non-local tchniqu is comparing with original documnt which is cland manually (ground truth imag).

4 Whr A and B ar two sts. RESULTS AND DISCUSSIONS Quantity of th imag is vry important whn claning th documnt with any local or non-local thrsholding algorithm bcaus during th procss som of th usful information is lost along with nois. So th givn quantitativ mtrics Misclassification, Rgion Nonuniformity, Rlativ forground ara rror, Hausdorff distanc, Jaccard distanc will provid th amount of information which w lost from th cland documnt by using any local/non-local tchniqu is comparing with original documnt which is cland manually (ground truth imag).th abov quality mtrics ar tstd on 0 ancint Tlugu and English txt sampls aftr claning ths documnts with MIGT algorithm. Initially th txt sampls ar cland by MIGT algorithm; during th claning procss som of th usful information along with background nois is liminatd. So th lost information from th imag is calculatd by using th abov said mtrics. In this connction th cland documnt is compard by ground truth imag which is obtaind from th binary imag and rmoving th unncssary information manually. Original txt sampls ar dscribd in Fig. and. Rsultant imags of MIGT algorithm for Tlugu and English ar givn in Figs. & and thir subsqunt ground truth imags ar shown in Figs. &. Figur Tlugu Ground-Truth Binary Txt Imag Figur English Txt Sampl Figur Rsultant Imag Of MIGT Algorithm For English Txt Sampl Figur Tlugu Txt Sampl Figur- English Ground Truth Binary Txt Imag In this rgard th tstd valus of Tlugu and English txt sampls ar tabulatd in tabl and tabl. Whn w obsrv both th tabls thr is a corrlation btwn th valus of diffrnt mtrics for both Tlugu and English txt sampls. Espcially thr is a similarity btwn th valus of Jaccard distanc and rgion nonuniformity for both th sampls. Figur Rsultant Imag Of MIGT Algorithm For Tlugu Txt Sampl

5 Tabl. Quantitativ Mtrics For Tlugu Txt Sampls Tabl. Quantitativ Mtrics for English Txt Sampls Misclass ification (Tlugu) Rgion nonuniformi ty Rlativ forgrou nd ara Hausdo rff distanc Jaccar d distanc Misclas sificati on (Englis h) Rgion Nonuni formity Rlativ Forgrou nd ara Hausdo rff Distanc Jaccard Distanc

6 . CONCLUSION W idntifid som similarity in th valus of mtrics which ar tstd on 0 Tlugu and English txt sampls which can b visualizd through th tabl. Basd on th availabl information from th tabls & th loss of information is sam in all sampls. Hr w hav not mad any comparison with othr thrshold mthods but basd on th mtric valus w cam to a conclusion IGT mthod is bttr suitabl for ancint documnts.. FUTURE SCOPE OF WORK Thr is a possibility to xtnd th abov procdur for nois fr sampls which ar contaminatd manually by diffrnt noiss at diffrnt lvls lik pppr, Gaussian nois and so on. Thn clan th noisy documnts with IGT as wll as othr mthods and thn find th quantitativ mtrics for idntifying th loss of information during th claning procss. REFERENCES [] M. Kaml and A. Zhao, Extraction of binary charactr/graphics imags from grayscal documnt imags, Graph. Modls Imag Procss, vol.,no,pp 0,. [] Abak, U. Baris, and B. Sankur, Th prformanc of thrsholding algorithms for optical charactr rcognition, Intl. Conf. Documnt Anal. Rcog. ICDAR, pp. 00,. [] N Vnkata Rao, A V Srinivasa Rao, S Balaji, L Pratap Rddy, Claning of Ancint Documnt Imags using Modifid Itrat Global Thrshold, IJCSI, Vol.8, Issu, Novmbr 0 [] M. Szgin and B. Sankur, Comparison of thrsholding mthods for non-dstructiv tsting applications, IEEE ICIP 00, Intl. Conf. Imag Procss., pp., 00 [] M. Szgin, and B. Sankur, Survy ovr Imag Thrsholding Tchniqus and Quantitativ Prformanc Evaluation, Journal of Elctronic Imaging, January 00, vol., no., pp. -8. [] YANG Gao-bo, ZHANG Zhao-yang Objctiv Prformanc Evaluation of Vido Sgmntation Algorithms with Ground- Truth. Journal of Shanghai Univrsity (English Edition ), 00, 8() : 0- [] K. Ntirogiannis, B. Gatos and I. Pratikakis. An Objctiv Evaluation Mthodology for Documnt Imag Binarization Tchniqus. Th Eighth IAPR Workshop on Documnt Analysis Systms, 008, - [8] Chun Ch Fung and Rapporn Chamchong. A Rviw of Evaluation of Optimal Binarization Tchniqu for Charactr Sgmntation in Historical Manuscripts Third Intrnational Confrnc on Knowldg Discovry and Data Mining. 00, -0 [] Štpán Šruba Comparison of sgmntation valuation mthods Confrnc on Computr Graphics, Visualization and Computr Vision, Postr procdings,0. [0] Ruchika Sharma, Balwindr Singh A Rviw on Evaluation of Binarization Tchniquson Camra-Basd Imags Intrnational Journal of Computr Scinc And Tchnology,vol,issu, 0,pp 0-0. [] Vincnt Rabux, Quality valuation of dgradd documnt imags for binarization rsult prdiction Intrnational Journal ond Dcumnt Analysis and Rcognition, pp -, 0. [] A V Srinivasa Rao Sgmntation of Ancint Tlugu txt documnts Intrnational Journal of Imag Graphics and Signal Procssing, 0, 8- [] Y.J.Zhang, A survy on valuation mthods for imag sgmntation, Pattrn Rcognition, Vol:, no.8, pp:-,.

Observer Bias and Reliability By Xunchi Pu

Observer Bias and Reliability By Xunchi Pu Obsrvr Bias and Rliability By Xunchi Pu Introduction Clarly all masurmnts or obsrvations nd to b mad as accuratly as possibl and invstigators nd to pay carful attntion to chcking th rliability of thir