D.Manjula Dept. of Computer Science and Engineering College of Engineering, Anna University Chennai, India

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1 Chtraala Gopalan et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Sldng wndow approach based Text Bnarsaton from Complex Textual mages Chtraala Gopalan (Correspondng Author) Dept. of Computer Scence and Engneerng Easwar Engneerng College, Anna Unversty Chenna, Inda D.Manjula Dept. of Computer Scence and Engneerng College of Engneerng, Anna Unversty Chenna, Inda Abstract Text bnarsaton process classfes ndvdual pxels as text or bacground n the textual mages. Bnarzaton s necessary to brdge the gap between localzaton and recognton by OCR. Ths paper presents Sldng wndow method to bnarse text from textual mages wth textured bacground. Sutable preprocessng technques are appled frst to ncrease the contrast of the mage and blur the bacground noses due to textured bacground. Then Edges are detected by teratve thresholdng. Subsequently formed edge boxes are analyzed to remove unwanted edges due to complex bacground and bnarsed by sldng wndow approach based character sze unformty chec algorthm. The proposed method has been appled on localzed regon from heterogeneous textual mages and compared wth Otsu, Nblac methods and shown encouragng performance of the proposed method. Keywords- Bnarzaton; Character segmentaton; textured bacground; Iteratve thresholdng; Edge spread analyss. I. INTRODUCTION Understandng texts from natural scenes/scene text mage such as, commercal sgnboards, traffc sgns, and advertsng bllboards s very useful n many purposes such as assstant system for mpared persons, drawng attenton of a drver to traffc sgns, text translaton system for foregners, potental applcatons le lcense plate recognton, dgtal note tang, document archvng and wearable computng. Bnarzaton problem concerns classfyng ndvdual pxels as text or bacground. Bnarzaton s necessary to brdge the gap between localzaton and recognton by OCR. The output of ths step s a bnary mage where blac text characters appear on a whte bacground. Current technques are categorzed nto two groups: global bnarzaton and local bnarzaton or adaptve bnarzaton. In global bnarzaton methods [], global thresholds are used for all pxels n mage and are not sutable for complex and degraded document mages. Global methods are fast and robust for small text. In the other hand, local bnarzaton methods change the threshold adaptvely over the mage accordng to propertes of local regons. Local bnarzaton methods are proposed to overcome bnarzaton drawbacs n global ones. Local bnarzaton methods can be mproved by calculatng local thresholds wthn separate wndows or areas [] [5]. In most of these methods, the sze and shape of the wndow are predefned parameters. Poor bnarzaton results are obtaned when a wndow s boundares cross characters and may gve rse to broen characters and vods, whch may cause undesrable artfacts n the bnary mage. And other challengng ssue related to bnarsng text from textual mages s the presence of complex/textured bacground. Here sldng wndow approach based bnarsaton method s proposed to bnarse the text from color documents wth textured /complex bacground. The paper s organzed as follows. Secton deals wth pror and related wors, Secton 3 llustrate our method wth varous modules n Secton 4-6. In Secton 7, expermental results are reported and conclusons and future wors are summarzed n Secton 8. II. RELATED WORKS Varous text bnarzaton technques have been found n lterature and are dscussed here. Dual bnarzaton method s proposed n [] whch can easly segment texts wth dfferent two color polartes from bacgrounds n the ey capton area. [0] Proposed a bnarsaton method to remove the bacground pxels nsde the characters also. The fnal bnary mage s gotten by fusng the three bnary mages such as the locally adaptve seed-fll method, the locally adaptve thresholdng method and the stroe-model-based method. A learnng-based bnarzaton method s proposed n [3] for same-type documents. In ths paper, the stroe wdth s used to evaluate the bnarzaton.ths approach can be used for only same type of documents. In [4] technque based on a Marov Random Feld (MRF) model of the document s proposed. The model parameters (clque potentals) are learned from tranng data and the bnary mage s estmated n a Bayesan framewor. In [5] Segmentaton of text n the detected text regon s performed wth all color components nto two dstnctve colors to dscrmnate between text and other non-text regon wth fuzzy c-mean (FCM) clusterng to depct the color dstrbuton. Adaptve local thresholdng based on a verfcaton-based mult threshold probng scheme s proposed n [6]. Ths approach s regarded as nowledge-guded adaptve thresholdng. [7] proposed a text segmentaton method based on spectral clusterng and the hstogram of ntensty s used for the object of groupng. Ths algorthm uses the normalzed graph cut measure as the thresholdng prncple to dstngush an object from the bacground. However these methods manly wor on the mages of text wth nearly unform bacground. Some wors deal wth complex bacground [8] [9] [3] and bnarzaton appled on ISSN :

2 sngle character [9] and on each word [8]. But they assume the text color to be unform and not sutable for color documents wth multcolored text. [] deals wth multcolored text documents regardless of the polarty of foreground-bacground shades wth edge-box analyss. However, f the bacground s textured, the edge components may not be detected correctly due to edges from the bacground and edge-box flterng strategy fals. Therefore, t s proposed to address the above ssues by proposng the Sldng wndow based character sze unformty chec algorthm to mnmze the complexty of bacground. III. SYSTEM DESCRIPTION Here, an approach s proposed n whch teratve thresholdng s used to detect edges nstead of fxed global threshold whch wll usually wor well for mages wth unform bacground, but not for textured bacground. Here unformty of character szes s analyzed to remove false edges due to textured bacground. Proposed bnarzaton technque conssts of the followng processes: Preprocessng, Iteratve thresholdng for edge detecton, Edge box formaton, False Edge box removal and bnarzaton by Sldng wndow algorthm as shown n Fg.. Sutable preprocessng technques are appled here frst to ncrease the contrast of the mage and blur the bacground noses due to textured bacground. Then edges are detected wth teratve thresholdng and boundng box s generated for the detected edges. Then false edge boxes are removed and mage s bnarsed by checng the unformty of character box szes. IV. PREPROCESSING The man objectve of the preprocessng step s to mae the foreground objects more clear than the bacground so as to help further edge detecton stage to gve canddate text edges clearly. Here, orgnal color mage s transformed to a grey level mage and followed by contrast enhancement based on entropy calculaton, condtonng by smoothng and grey scale extenson [7]. A. Condtonal Contrast Enhancement Images taen under a poor lghtng condton may result n low entropy, whch needs ncrease n contrast of the mage for better processng. Therefore, entropy can be used as an ndcaton f an ncrease n the contrast of the mage wll be necessary. If the entropy calculated of the mage s too low, the contrast can be ncreased, otherwse the detected edges of characters may not form closed shapes. Entropy can be computed as follows: H p log p p log () p H denotes the entropy of an mage; p represents the proporton of greyscale values n the range [0.55] over the Chtraala Gopalan et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng entre mage. From extensve experments, an emprcal value of Hthres = 38 s recommended to dentfy mages, whch needs to be enhanced n terms of the contrast. Fg. System Archtecture of the proposed method Based on ths threshold, the contrast of the mage needs to be ncreased as follows: C ( x, y ) exp 55 avg _ T T ( x, y ) v C(x, denotes the transformed results. avg_t s the average grey values n the mage represented by T. The parameter υ can be fxed at υ = 5. B. Smoothng Localzed Image False EB Removal Sldng wndow algorthm Bnarsed mage Preprocessng Iteratve thresholdng Eroson Edge detecton Edge Box (EB) formaton Then a smple smoothng/blurrng process s appled to mnmze the effects of these dsturbances on the edge detecton process and grey scale extenson s performed for the condtoned mage to ncrease further contrast. S( x, ( C( x, M ) (3) 0 Where C(x, and S(x, denote the greyscale value at poston (x, of the mage before and after the transformaton. M represents the mas [7]. C. Grey scale extenson Maxmum and mnmum greyscale values of the smoothed mage can be computed. If max_s and mn_s are close to each other, e.g., max_s mn_s < 80, the greyscale mage wll be monotonous wth a low contrast. Greyscale extenson wll ncrease contrast va explotng the full range max_s mn_s = 55. Transformaton equaton s gven as, () ISSN :

3 Chtraala Gopalan et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng G ( x, ( S ( x, ) (4) Where S s the grayscale mage after the smoothng transformaton and G represents the mage followng extenson. mn_ S, 55 max_ S mn_ s Processed mage after all these steps wth nput mage are shown Fg. a to d. V. ITERATIVE THRESHOLDING AND EDGE DETECTION Now edges are to be detected from the preprocessed mage. Conventonal edge detecton algorthms belong to the hgh pass flterng, whch are not ft for complex bacground mages. Fxed global threshold wll usually wor well for mages wth unform bacground, but not for textured bacground and the contrast of objects vares wthn the mage.in such cases, t s convenent to use a threshold gray level that s a slowly varyng functon of poston n the mage[4]. And also edge contnuty to be mantaned and edge overlappng to be avoded. In ths aspect, an teratve thresholdng algorthm and morphologc erode algorthm [5] are used here to detect the edges. Followng steps are used to detect edges n teratve thresholdng, 4. New threshold T + s calculated as average of Z and Z. Z Z T (0) 5. The algorthm s completed f T = T + otherwse let =+ and go bac to step. Then morphologcal eroson s appled whch ensures that the detected edges are only one sngle pxel wde and maes the detecton more accurately. Meanwhle, ths avods the edge overlappng caused by the ncrease of the edge wdth. After the eroson for the mage the edges are extracted n the mage. Because the eroson operator elmnates all the boundary ponts from an object and the boundary ponts are all one sngle pxel wde. If the eroded mage s subtracted by the orgnal mage, the result wll be one sngle pxel wde edge. Fg.e, f and g show the result of the edge detecton after teratve thresholdng and eroson. An 8-connected component labelng follows the edge detecton step and the assocated boundng box nformaton s computed. Each component, thus obtaned, s termed as an edge-box (EB) as shown n Fg. 3a. (a) Input mage (b) After Contrast enhancement of grey mage 0 0. Intal threshold T, T { T 0} s calculated usng mnmal and maxmal gray value of the mage. 0 Zmn Zmax T (5). The mage s segmented nto two parts (R and R) by the threshold T R { f( x, f( x, T } (6) R { f( x, 0 f( x, T } (7) (c) After Smoothng (e) Iteratve thresholded mage (d) After grey scale extenson (f) Eroded mage 3. Average gray level value of R and R s calculated and denoted as Z and Z separately. Z Z T f (, j) x N (, j) T T T N (, j) f (, j) x N (, j) N (, j) (9) Where f(,j) s the gray level value of pont (,j) n the mage, N(,j) s the power coeffcent of pont (,j) and t equals to.0 commonly. (8) VI. (g) Detected edge Fg. Edge detecton FALSE EDGE REMOVAL AND BINARISATION BY SLIDING WINDOW APPROACH Now Each EB s checed for varous characterstcs of ndvdual text character (Taml and Englsh) and EBs not fulfllng the crtera are removed. The followng characterstcs are consdered for ndvdual text character to flter out non text EBs: ISSN :

4 Chtraala Gopalan et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng. The aspect rato s constraned to le between 0. and 0 to elmnate hghly elongated regons.. If a partcular EB has exactly one or two EBs that le completely nsde t, the nternal EBs can be convenently gnored as t corresponds to the nner boundares of the text characters. On the other hand, f t completely encloses three or more EBs, only the nternal EBs s retaned whle the outer EB s removed as such a component does not represent a text character. Thus, the unwanted components are fltered out by subjectng each edge component to the followng constrants [] : f (N nt <3) {Reject EB nt, Accept EB out } else {Reject EB out, Accept EB nt } () A. Sldng Wndow approach based false EB removal and Bnarsaton Characters wll have approxmately unform sze n the localzed regon. Ths property s used here to remove non character EBs wth the proposed Sldng wndow approach based bnarsaton. Steps nvolved n the algorthm are as follows: a) Sze of varous EBs are calculated and store n array A [ ]. b) Intally wndow over the array elements s assumed by coverng frst two elements A[] as a left ponter (LP) and A [+]) as a rght ponter (RP). c) If the dfference between LP and RP s greater than threshold, consder only RP and mae t as LP and slde the wndow sze to cover next element and chec the dfference between those two elements. 3. Now bnarse the mage wth threshold (T s ). EBs wth sze lesser than T s are removed. Foreground text pxels are shown as whte and bacground as blac pxels. EB = {If sze (EB) >= Ts} EB = 0 {If sze (EB) <= Ts} () Processed mage after these steps s shown n Fg 3a. Fg 3(a) Edge box (b) Bnarsed mage VII. RESULTS AND DISCUSSION In ths secton, the results of the proposed algorthm are presented. The performance of the proposed method s compared wth two well-nown thresholdng methods, ncludng Otsu [] and Nblac [] algorthms. We show here some examples wthout OCR comparson. We beleve a vsual evaluaton s suffcent for a qualtatve estmaton of our method. The results show that the proposed method s an effectve method and outperforms the other methods n the complex / textured bacground stuaton. Dsturbances due to the textured bacground are well avoded n our sldng wndow method than the compared ones. So that bnarsed mage can be better recognzed by OCR. (a) (b) Input mage Otsu method d) Compare left end marer wth remanng elements one by one and f the dfference < th, expand wndow by ncludng those elements nto the wndow and freeze the wndow once the condton fals. Ths wndow contans dentfed unform szed character EBs between LP and RP. (c) Nblac method e) Then compute mnmum of the elements wthn that frst freezed wndow (M W ). Here, mnmum value s chosen so as to allow mxed szed characters. (d) Proposed Sldng wndow method Fg 6 Performance Comparson of the proposed method wth other methods f) Then slde the wndow to chec the remanng elements and determne other vald wndows (W ) and mnmum value of those wndows as M W, M W, M W g) Compute mnmum of M W whch gves unform edge box sze as the threshold T s. ISSN :

5 VIII. CONCLUSION Here a novel method s proposed to bnarze text from color mages wth textured bacground by analyzng character and non character edges. Sldng wndow based method s proposed to dentfy the character edges by suppressng the unwanted non character edges. Expermental results are showng encouragng performance of the proposed method wth the compared bnarzaton algorthms. REFERENCES [] Cheolon Jung and Joongyu Km, Player Informaton Extracton for Semantc Annotaton n Golf Vdeos, IEEE Transactons on broadcastng, vol. 55, no., March 009, pp [] Kasar, J Kumar and A G Ramarshnan, Font and Bacground Color Independent Text Bnarzaton, Second Internatonal Worshop on Camera-Based Document Analyss and Recognton (CBDAR007), September, 007. [3] Yuanpng Zhu, Augment document mage bnarzaton by learnng, 9th Internatonal Conference on Pattern Recognton, ICPR 008.pg [4] Wolf, C. Doermann, D, Bnarzaton of low qualty text usng a Marov random feld model, 6th Internatonal Conference on Pattern Recognton, 00, vol 3, pp [5] Jonghyun Par, Toan Nguyen Dnh, and Gueesang Lee, Bnarzaton of Text Regon based on Fuzzy Clusterng and Hstogram Dstrbuton n Sgnboards, Proc. world academy of scence, engneerng and technology vol. 33 September 008 pg [6] Xaoy Jang, Computer Socety, and Danel Mojon, Adaptve Local Thresholdng by Verfcaton-Based Multthreshold Probng wth Applcaton to Vessel Detecton n Retnal Images, IEEE Transactons on Pattern Analyss And Machne Intellgence, vol. 5, no., January 003, pg [7] Ru Wu, Janhua Huang, Xanglong Tang, Jafeng Lu, A Text Image Segmentaton Method Based on Spectral Clusterng, Computer and Informaton Scence, Vol,No.4,November 008. [8] Anh-Nga La GueeSang Lee, Bnarzaton by Local K-means Clusterng for Korean Text Extracton, IEEE Internatonal symposum on sgnal processng and nformaton technology 008, pp. 7- [9] Huang, Huadong Ma, He Zhang, A New Vdeo Text Extracton Approach IEEE Internatonal Conference on Multmeda and Expo, 009. ICME 009. [0] Wuy Yang Shuwu Zhang Zh Zeng Habo Zheng, Method combnaton to extract text from mages and vdeos wth complex bacgrounds, Internatonal Conference on Audo, Language and Image Processng, 008. ICALIP 008. pg Chtraala Gopalan et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng [] W. Nblac. An Introducton to Dgtal Image Processng. Prentce- Hall, Englewood Clffs, New Jersey, 986 [] N. Otsu. A threshold selecton method from gray level hstogram. IEEE Transactons on System, Man, Cybernetcs, 9():6 66, January 978. [3] Egyul Km, SeongHun Lee, JnHyung Km, Scene Text Extracton usng Focus of Moble Camera 009 0th Internatonal Conference on Document Analyss and Recognton,pg: [4] Kenneth R. Castleman. Dgtal Image Processng. Prentce-Hall Internatonal, Inc., Be Jng, BJ,998. [5] Yan Wenzhong, Shen Shuqun, An Edge Detecton Method for Chromosome Images, The nd Internatonal Conference on Bonformatcs and Bomedcal Engneerng, 008, pp [6] M. Sarfuddn and R. Mssaou. A New Perceptually Unform Color Space wth Assocated Color Smlarty Measure for Content-Based Image and Vdeo Retreval. In Proc. of ACM SIGIR 005 Worshop on Multmeda Informaton Retreval (MMIR 005), pp 8, 005. [7] Y Zhang and Ko Kong Tan, Text extracton from mages captured va moble and dgtal devces, Int. J. Computatonal Vson and Robotcs, Vol., No., 009,pp AUTHORS PROFILE Chtraala Gopalan receved the B.E. and M.E degree from Unv. of Madras,Inda n 995 and 00 respectvely. From 995 to 998 she wored as a lecturer n Raja rajeswar Engneerng college,taml nadu, Inda. From 998 to 00 she wored as a lecturer n Easwar Engneerng college and Snce 00, she s worng as an Assstant Professor n computer scence and Engneerng, at Easwar engneerng college, Taml nadu, Inda. Currently she s Pursung Ph.D n Anna unversty, Chenna, Inda n the department of computer scence and Engneerng. Her research areas of nterest are Dgtal Image processng, Data mnng and Natural language processng. Dr.D.Manjula ganed her B.E degree from Thyagarajar College of Engneerng n 983 and M.E & Ph.D degree from Anna unversty, Chenna n 987 & 004 respectvely. She s an Assstant professor n Department of Computer scence and Engneerng, Anna unversty Chenna, Taml nadu,inda. At present, she teaches and leads research towards language technologes, Data mnng, Text mnng, Imagng and networng. ISSN :