Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 COMPARING ACKGROUND ELIMINAION APPROACHES FOR PROCESSING OF ANCIEN HAI MANUSCIPS ON PALM LEAVES RAPEEPORN CHAMCHONG, CHUN CHE FUNG School of Informaton echnology, Murdoch Unversty, Murdoch 65, WA E-MAIL: rapeeporn.c@gmal.com, l.fung@murdoch.edu.au Abstract: he objectve of the Preservaton of Palm Leaf Manuscrpts Project at Mahasaraham Unversty at haland s to preserve and retreve tradtonal nowledge from ancent manuscrpts recorded on palm leaves. An essental tas n the process s to recognze the ancent characters automatcally through mage processng technques. he paper compares dfferent bacground elmnaton approaches whch could be used. he am s to mprove the global and local adaptve thresholdng technques ntellgently, and to form the pre-processng procedure n the automated process. Keywords: acground Elmnaton; narzaton; hresholdng echnque; Document Segmentaton; ha ancent manuscrpts. Introducton Palm leaf manuscrpts were one of the popular wrtten documents for over a thousand years n South and Southeast Asa [, ]. In ha hstory, dred palm leaves have been used to record uddhst teachng and doctrnes, follores, nowledge and use of ndgenous medcnes, stores of dynastes, tradtonal arts and archtectures, astrology, astronomy, and technques of tradtonal massages. Recently, several unverstes and nsttutes ncludng medcal departments and relgon organzatons have ntated projects to collect, recover and preserve ha palm leaf manuscrpts. It s recognzed that these documents contan nvaluable nowledge, hstory, culture, and local wsdoms of ha cvlzaton. In partcular, nowledge concernng ndgenous medcnes has been studed wth great attenton due to ther potental n treatng many alments and dseases. Wth the passage of tme, most of these palm leaves, f left unattended, wll deterorate as they are comng to the end of ther natural lfetme as they face destructve elements such as dampness, fungus, bactera, ants and cocroaches. For ths reason, Mahasaraham Unversty s establshng the Palm Leaf Manuscrpt Preservaton Project [3] for the dscovery, preservaton and protecton of palm leaf manuscrpts from Northeast haland and to extract nowledge from the ancent world. Currently, computer technology can store and process the ancent mage documents n multmeda systems. It s possble to collect and access those manuscrpts and preserved them n dgtal formats n the computer. Although currently storng systems can store document mages, there s no specfc system to retreve the nowledge from these ancent documents. It s the ultmate am of the project to develop an effcent mage processng system that could be used to retreve nowledge and nformaton from the palm leave mages. hs research wll apply mage processng and ntellgent technques to analyze and retreve nowledge from these manuscrpts. However, t s recognzed that t s not an easy tas as there are many styles of tradtonal ha handwrtng, nose on the mages, and fragmentaton or cracs due to fraglty of the aged leaves. It s common that mages of the collected ancent documents are of poor qualty due to nsuffcent attenton pad to the condton of the storage and the qualty of the wrtten materal. As a result, the foreground and bacground n the scanned mages are dffcult to be separated. Many of the palm leaf mages have varyng contrast and llumnant, smudges, smear, stans, and contamnatons due to seepng n from the other sde of the palm leaf. Pror to the stage of nowledge extracton, characters or text on the mages have to be recognzed. here are three steps whch need to be completed pror to the tas of character recognton. Frst, a palm leaf s scanned nto a RG mage and then s s converted to become a gray-scale mage. Next, mage enhancement s used to enhance the qualty of mage. he Gaussans flterng technque [4, 5] s one of the commonly used technques. After ths stage, bacground elmnaton s appled and then text and character separaton are carred out. acground elmnaton, also nown as bnarzaton, 978--444-373-/9/$5. 9 IEEE 3436 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.
Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 s an essental part of preprocessng step n mage processng, convertng gray-scale mage to bnary mage, whch s then used for further processng such as document mage analyss and optcal character recognton (OCR). Consequently, bacground elmnaton of ancent document s crucal to remove unrelated nformaton, bacground or nose on the documents. If ths step s nsuffcent, orgnal characters from the mage may be lost or more nose may be added. Furthermore, ths technque s essental to mprove the readablty of the documents and the overall performance of the process. In ths paper, a number of the classcal or the most commonly used approaches based on the global and local adaptve thresholdng technques are appled to several example real data set whch has been collected by [3]. A new framewor to be appled for bacground elmnaton s also proposed.. hresholdng echnques narzaton s the process of convertng a gray-scale mage to a bnary mage by usng threshold selecton technques to categorze the pxels of an mage nto ether one of the two classes. here are two man technques of bnarzaton [6] and they are global thresholdng and local adaptve thresholdng technques... Global hresholdng echnques Global thresholdng technques [6] attempt to fnd a sutable sngle threshold value (hr) from the overall mage. he pxels are separated nto two classes: the foreground (text whch s blac color) and the bacground (whte color). hs can be expressed as follows blac f If (x, y) hr I b (x, y) = () whte f If (x, y) > hr whch I f (x,y) s the pxel of the nput mage after nose reducton process and I b (x,y) s the pxel of the bnarzed mage. he well nown global thresholdng technque s Otsu s algorthm [7]. hs research compares two approaches of global thresholdng technque whch are the basc Otsu s algorthm and the recursve Otsu s algorthm whch was purposed by Cheret and et al [8]. Further detals of these two technques are gven n subsequent sectons... Local Adaptve hresholdng echnques Local adaptve thresholdng technques [6] calculate the threshold values whch are determned locally based on pxel by pxel, or regon by regon. A threshold value (hr(x,y)) can be derved for each pxel n the mage, and the mage can be separated nto foreground and bacground as gven n expresson (). I b (x, y) = blac f I f (x, y) hr(x, y) () whte f I f (x, y) > hr(x, y) (hr(x,y)) n the above expresson s dfferent from hr n expresson () as ts value vares accordng to the local regon. he conventonal local adaptve thresholdng technques are Nblac s algorthm [9], Sauvola s algorthm [] and local adaptve mean-c []. hese three technques are expermented n ths study. An mproved algorthm has also been proposed and s descrbed n the later sectons. 3. Otsu s Algorthm hs algorthm was proposed by Otsu [7] and t s based on hstogram analyss. he threshold selecton s processed as a clusterng process whch dvdes all the pxels of an mage nto two classes: C, s the foreground or text, wth a gray-level value below or equal to hr, and C s the bacground wth a gray-level value above hr. y conventon, the values below hr are blac and those above are whte. A measurement of the goodness of the threshold value, hr, s based on the dscrmnant crtera maxmzng (η), whch s the separablty measure as shown below. η = σ σ (3) η s the rato of between-class varance ( σ ) and total varance ( σ ) of the resultant classes n gray levels, and where σ σ = L ( µ ) = ω ( µ µ p ) = ω ω ( µ µ ω, ) µ = Pr( C) = p L = p + ω ( µ µ = ω ) (4) (5) (6) 3437 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.
Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 L = Pr( C) = p = ω + µ ω = µ ω p (7), µ = (8) µ µ µ = (9) ω L p = n N, p, p () where p s the probablty of occurrence of gray-level, n s the number of pxels wth gray-level and N s the total number of pxels. Consequently, the optmal threshold * s the maxmum value η(*) rangng between and ( η ) or equvalently maxmzed σ that: ( ) = σ () σ η () and σ (*) = max σ () < L () Whle the Otsu s algorthm has been successfully appled to many applcatons, there are stuatons where the mage may have multple bacground. hs has led to an modfcaton of the basc approach and became the recursve algorthm as shown n the next secton. 4. Recursve Otsu s Algorthm In ths algorthm as proposed by Cheret and et al [8], the thresholds selecton s determned automatcally by a recursve applcaton of Otsu s algorthm. he recursve crteron s on the separablty (η) measure. It was recommended that the recurson stops when the bacground s separated by the threshold value of η.95, or when η does not change, ndcatng the bacground s havng a consstent gray level. he procedure of the threshold selecton can be summarzed as follows:. Intalze t =, η =, where t s the number of tmes of the recurson.. Calculate the hstogram of the fltered mage f f (, j) usng the expressons (3) to () as gven above. 3. Select the threshold, hr(t), that maxmzes the separablty ( η = σ σ ) as stated n expresson (3). 4. hr(t) s the value that separates the mage nto regons - the bacground (C t- ) and the object (C t ). 5. Increment t, and calculate the new hstogram of the mage Ct. Perform steps 3 to 5 untl η.95 or η does not change. he total of mage regon before elmnate bacground s a unon of C t I f = U C t (3) t {,..,p} where C t s sub-regon mage and the result of bacground elmnaton (object regon) s gven by I b I b = C p (4) where C p s the fnal mage that a threshold selecton that has been determned by the sutable threshold value, η. he subsequent secton descrbes the local adaptve thresholdng technques. 5. Local Adaptve Mean-C A smple approach to modfy the thresholdng value adaptvely s to use a value, whch statstcally examnes the ntensty values of the local neghborhood of each pxel. he statstcal technque whch s most approprate n computng the threshold, depends largely on the mage tself. One way s to use the mean of the local ntensty dstrbuton n a fxed overlappng wndow together wth a constant value, C.. he hreshold value can be calculated from the dfference between the mean value and the constant value, C,.e. = mean-c []. hs s descrbed n the followng steps:. Convolve the wndow of the mage wth a sutable statstcal operator,.e. the mean or medan. Subtract the orgnal from the convolved mage 3. hreshold the dfference mage wth the value, C 4. Invert the thresholded mage. he sze of the wndow for use s very mportant. It must be large enough to cover a suffcent neghborhood, yet f chosen too large t can overloo mportant local varatons. Smlarly, the value of C could be subject to dscreton. 6. Nblac s Algorthm Nblac s algorthm [9] s based on the varyng threshold over the mage by usng local mean value, m and the standard devaton, s, of gray level n a small neghbourhood or wndow of each pxel. A threshold for each pxel can be calculated from (x,y) = m(x,y) + * s(x,y) (5) where (x,y) s the threshold value for pxel at (x,y), m(x,y) and s(x,y) are the mean and standard devaton of the 3438 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.
Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 neghborhood of (x,y). s a negatve constant (-.) whch s defned by user. he value of [] s used to determne how much of the object boundary s taen as a part of the gven object. hs method can separate the object or text from the bacground effectvely n the areas near to the object. However, n ths approach, nose stll occurs n a varyng manner n the bacground. Furthermore, the sze of the wndow s mportant. It should be small enough to preserve local detal and large enough to suppress nose that the wndow should cover at least - characters. 7. Sauvola s Algorthm Sauvola and Petanen [] modfed the threshold value of Nblac s algorthm to elmnate bacground nose by addng a hypothess on the gray values of text and bacground pxels n the followng expresson gven n ther paper. s(x, y) (x, y) = m(x,y) + R (6) In ther experment, s a postve value set at.5 and R s the dynamc range of standard devaton. ased on 8-bt gray level mage, the value of 8 was used. It was clamed that the values helped to remove the effects of stans on the mage. Dfferent values of parameters have been used n ths study. 8. Proposed Method ased on the local adaptve thresholdng technques, a threshold value s assocated wth each pxel. However, n ths study, t was found that s the approaches have ntroduced more nose n the bacground and also lost some of the data or text. he soluton to reduce the nose and to sharpen the text s to calculate the mean value and standard devaton of all local thresholds, and then adjust threshold value n each pxel by the followng expresson: (x, y) f m() - (w *S()) < (x, y) < m() - (w *S()) (x, y) = (7) m() otherwse where w s weght whch s selected by the user. In ths case, w =.5 was used. It s also found that the approach also managed to recover some of the texts whch have lost n the prevous approaches. he next secton provdes results from three samples of manuscrpts on ancent palm leaves. hey are followed by a dscusson on the comparson. 9. Expermental Results In ths experment, 99 mages were used and they have been scanned from real palm leaf manuscrpts of more than years old. he resoluton of the nput mages s x dp n RG format. he nput mages were converted to gray-scale mages and then nose s reduced by Gaussans flterng technque. hs secton shows the results of bacground elmnaton by comparng the proposed technque wth those of Otsu, recursve Otsu, local adaptve mean-c, Nblac and Sauvola. In ths experment, the proposed technque s appled to Sauvola s algorthm to llustrate ts ablty to mprove the local adaptve thresholdng technques. he example results are shown as n Fgure -3.. Dscussons and Concluson In ths paper, we found that there s no sngle best bnarzaton technque sutng all mages. Some algorthms can wor better n some mages but not wth the others. As the data set s real data, one cannot control the nput mages and subsequently one cannot clam whch algorthm and what threshold value should be the best choce. However, Otsu s algorthm gves a good result n many cases but ths technque wll gve bad result as a blac mage after bnarzaton. It s found that Otsu s algorthm can be mproved by usng recursve Otsu s algorthm but t also elmnates part of the text. Mean-C gves good result as well but there are more pepper nose surrounded the character (or bacground). Nblac s algorthm s not good for ths case that there s more nose and darer mages. Sauvola s algorthm s better than Nblac algorthm and when the parameters are adjusted, Sauvola s algorthm also has a problem about nose and character. If character s sharp, nose wll be ncreased. If nose s reduced, character wll be lost n some parts. Fnally, t was found that the proposed method can mprove Sauvola s algorthm. hs technque can enhance the qualty of character and reduce the nose. Not only Sauvola s technque, the proposed method also mproves other local adaptve thresholdng technques wth a reducton n the nose and recovery of some of the characters.. Future Wor As no sngle method can be used for all mages, how to choose the best bnarzaton technque for the user s the ey ssue. At present, applcaton of Relevance Feedbac (RF) approach s beng nvestgated n order to determne whch 3439 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.
Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 algorthm s more approprate by allowng the users to select the mages themselves. he machne wll then learn from the common features of the selected mages to determne whch algorthm wors better, and by clusterng mages whle algnng them wth the proposed algorthm. Next stage, algorthm or methodology for Lne, Word and Character Segmentaton wll be developed and RF wll also be appled to determne the best algorthm for the subsequent tass. a. Orgnal mage a. Orgnal mage b. Flterng mage b. Flterng mage c. Otsu s algorthm wth hr = 6 c. Otsu s algorthm wth hr = 77 d. Recursve Otsu s algorthm wth hr = 95 d. Recursve Otsu s algorthm wth hr = 9 e. Local Adaptve Mean-C e. Local Adaptve Mean-C f. Nblac s algorthm f. Nblac s algorthm g. Sauvola s algorthm g. Sauvola s algorthm h. he proposed technque wth Sauvola Fgure. A comparson of bacground elmnaton technques (Example ) h. he proposed technque wth Sauvola Fgure. A comparson of bacground elmnaton technques (Example ) 344 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.
Proceedngs of the Eghth Internatonal Conference on Machne Learnng and Cybernetcs, aodng, -5 July 9 support and provdng mages from the ancent manuscrpts. References a. Orgnal mage b. Flterng mage c. Otsu s algorthm wth hr = 46 d. Recursve Otsu s algorthm wth hr = 46 e. Local Adaptve Mean-C f. Nblac s algorthm g. Sauvola s algorthm h. he proposed technque wth Sauvola Fgure 3. A comparson of bacground elmnaton technques (Example 3) Acnowledgements We than the Preservaton of Palm Leaf Manuscrpts Project, Mahasaraham Unversty, haland for ther [] O. Surnta and R. Chamchong, "Image Segmentaton of Hstorcal Handwrtng from Palm Leaf Manuscrpts," n 5th IFIP Internatonal Conference on Intellgent Informaton Processng, ejng, Chna, 8, p. 8. [] Z. Sh, S. Setlur, and V. Govndaraju, "Dgtal Enhancement of Palm Leaf Manuscrpt Images usng Normalzaton echnques," n 5th Internatonal Conference On Knowledge ased Computer Systems, Hyderabad, Inda, 4. [3] "Palm Leaf Manuscrpt Preservaton Project, Mahasaraham Unversty," haland. [4] Z. Hussan, Dgtal Image Processng : Practcal Applcatons of Parallel Processng echnques: Ells Horrwood, 99. [5] E. R. Daves, Machne Vsson, heory, Algorthms, Practcaltes, nd ed.: Acadamc Press, 997. [6] O. D. rer and A. K. Jan, "Goal-Drected Evaluaton of narzaton Methods," IEEE rans. on Pattern Analyss and Machne Intellgence, vol. 7, pp. 9-, December 995 995. [7] N. Otsu, "A hreshold Selecton Method from Gray-level Hstogram," IEEE rans. Systems Man Cybernet, vol. 9, pp. 6-66, 979. [8] M. Cheret, J. N. Sad, and C. Y. Suen, "A Recursve hresholdng echnque for Image Segmentaton," IEEE rans On Image Processng, vol. 7, pp. 98-9, 998. [9] W. Nblac, An Introducton to Dgtal Image Processng: Prentce Hall, 986. [] J. Sauvola and M. Petanen, "Adaptve Document Image narzaton," Pattern Recognton, vol. 33, pp. 5-36,. [] R. Fsher, S. Perns, A. Waler, and E. Wolfart, "Adaptve hresholdng", HIPR, 4. [Onlne]. Avalable at http://homepages.nf.ed.ac.u/rbf/hipr /adpthrsh.htm. [Accessed: Jan., 9]. []. Gatos, I. Pratas, and S. J. Perantons, "Adaptve Degraded Document Image narzaton," Pattern Recognton, vol. 39, pp. 37-37, 6. 344 Authorzed lcensed use lmted to: Murdoch Unversty. Downloaded on October 3, 9 at 5:9 from IEEE Xplore. Restrctons apply.