A Two-stage and Parameter-free Binarization Method for Degraded Document Images

Transcription

1 A Two-stage and Paamete-fee Binaization Method fo Degaded Document Images Yung-Hsiang Chiu 1, Kuo-Liang Chung 1, Yong-Huai Huang 2, Wei-Ning Yang 3, Chi-Huang Liao 4 1 Depatment of Compute Science and Infomation Engineeing, National Taiwan Univesity of Science and Technology, Taipei, Taiwan, R. O. C. 2 Institute of Compute and Communication Engineeing, Jinwen Univesity of Science and Technology, Taipei, Taiwan, R. O. C. 3 Depatment of Infomation Management, National Taiwan Univesity of Science and Technology, Taipei, Taiwan, R. O. C. 4 System Online Co. Ltd., Taiwan, R. O. C. Abstact Binaization plays an impotant ole in document image pocessing, especially in degaded documents. Fo degaded document images, adaptive binaization methods often incopoate local infomation to detemine the binaization theshold fo each individual pixel in the document image. We popose a two-stage paamete-fee window-based method to binaize the degaded document images. In the fist stage, a poposed scheme is used to detemine a pope window size beyond which no substantial incease in the local vaiation of pixel intensities is obseved. In the second stage, based on the detemined window size, a noise-suppessing scheme delives the final binaized image by contasting two binaized images which ae poduced by two adaptive thesholding schemes depending on the change ate of the numbe of binaized foegound pixels. Empiical esults demonstate that the poposed method is competitive when compaed to the existing adaptive binaization methods and achieves bette pefomance in F-measue. Keywods: Adaptive binaization method, Degaded document image, Document image pocessing. 1. INTRODUCTION Document image pocessing is necessay fo stoing, tansmitting, and managing digital documents. Among diffeent types of document image pocessing, binaization is a peliminay pocess and the esultant binay images usually affect the pefomance of the succeeding pocesses, such as the document image segmentation, the optical chaacte ecognition, and so on. Fo binaization, each pixel in a document image can be classified as a foegound o a backgound pixel. Pixels inside chaactes, lines, and cuves in a document image ae foegound pixels and should be binaized as black pixels and the emaining backgound pixels should be binaized as white pixels. Fo maximizing the between-class vaiance of foegound and backgound pixels, Otsu [1] poposed an automatic thesholding scheme to detemine a global theshold fo the input image. It usually yield good esultant binay images. Howeve, the detemined global theshold may not be applicable fo degaded document images since intensities of foegound and backgound pixels ae contaminated at diffeent positions of the images. To alleviate the poblem caused by the degaded document images, adaptive binaization schemes [2, 3, 4, 5, 7, 8] which incopoate the infomation fom local statistics of an image ae poposed to impove the global thesholding method. Niblack [2] pesented a windowbased method to detemine the theshold fo each pixel by incopoating the infomation of the mean and the standad deviation of gay levels within each window. Sauvola and Pietikainen [3] modified Niblack s method by poposing diffeent weights on the mean and the standad deviation of gay levels within each window. Fo bluepint images, Zhao et al. [4] utilized geometic featues and poposed an efficient window-based thesholding method. Gatos et al. [5] binaize the document image by contasting the document image to the backgound suface which is constucted by intepolating the backgound pixels afte emoving the binaized foegound pixels via Sauvola and Pietikainen s method. Based on the edge map detected by the Canny edge-detecto [6], Chen et al. [7] binaized the input document image using a pai of high and low thesholds. Moghaddam and Cheiet [8] poposed a multi-scale windowbased thesholding scheme which fist geneates seveal binaized images based on diffeent window sizes and then iteatively combines the binaized images to yield the final binaized image. In this pape, we pesented a two-stage paamete-fee windowbased method to binaize the degaded documents. In the fist stage, a poposed scheme is used to detemine a pope window size beyond which no substantial incease in the local vaiation of gay levels is obseved. In the second stage, given the detemined window size, a noise-suppessing scheme delives the final binay image by contasting two binaized images which ae poduced by two adaptive thesholding schemes depending on the change ate of the numbe of foegound pixels. Empiical esults demonstate that the poposed method is competitive when compaed to the existing adaptive binaization methods and achieves bette pefomance in F-measue. 2. CHALLENGES IN ADAPTIVE BINARIZATION The adaptive binaization scheme needs to deal with two challenges: the detemination of a pope window size used to collect the local infomation and the tade-off between detail pesevation and noise suppession. These two challenges motivate the eseach of this pape and ae addessed in this section. The quality of the esultant binay document images poduced by the existing adaptive binaization methods often ae vey sensitive to the window size used [2, 3, 4, 5]. Pope window size usually depends on the scale of objects in the document images. The document images with lage objects equie lage window size in the adaptive binaization scheme. Binaizing an image as shown in Figue 1 with lage objects using smalle than necessay window size may eoneously binaize foegound pixels to backgound pixels as shown in Figue 1. Figue 1 (c) illustates a bette binaized esult of Figue 1 when a lage windows size is used. Howeve, adaptive binaization scheme with lage than necessay window size will not significantly incease the quality of the binaized images, as shown in Figue 1 (e) and (f), but incus highe computational cost.

2 (c) (d) (e) (f) Figue 1: The effect of window size when using Sauvola and Pietikainen s method. Document image with lage-scale chaactes. Binaized image of using a 9 9 window. (c) Binaized image of using a window. (d) Document image with small-scale chaactes. (e) Binaized image of (d) using a 9 9 window. (f) Binaized image of (d) using a window. Fo pope window size, Gatos et al. [5] suggest that window size should cove at least 1 to 2 chaactes. Howeve, detecting chaacte size usually equies image segmentation and is difficult fo degaded documents. Chen et al. [7] apply a 3 3 window and detemine two thesholds based on the edge pixels detected by the Canny edge detecto. The quality of the binaized image heavily depends on the coectness of the edge map which is usually poo fo degaded documents. Moghaddam and Cheiet [8] popose a scheme which stats with a lage window size detemined by the aveage line height of the input document image and iteatively educes to a pope window size. Since the aveage line height is usually detemined by the image segmentation pocess and the poposed scheme suffes fom the same poblem as Gatos et al. s method. Fee fom othe image pe-pocessings, we fist apply Otsu s method to obtain a ough foegound image and then detemine a pope window size based on the change ate of the vaiation of the foegound intensities within each window. In addition to detemining the pope window size, the tade-off between the pesevation of detailed contents and noise suppessing should be addessed in the adaptive binaization scheme. Let f be the input document image and the intensity value of the pixel at position (x, y) is denoted by f (x, y), 0 f (x, y) 1. Given a specific window of size w w with w = 2 + 1, the theshold used fo binaization in Niblack s method is expessed as TN ib,w (x, y) = µw (f, x, y) + kσw (f, x, y) (1) whee k is a use-defined paamete and µw (f, x, y) and σw (f, x, y) epesent espectively the mean and standad deviation of intensities of the pixels within the window centeed at (x, y) and can be expessed as µw (f, x, y) = 1 f (x + i, y + j), 2 w i= j= (2) (c) Figue 2: The effect of k when using Sauvola and Pietikainen s method. Degaded document image. Binaized image using k = (c) Binaized image using k = 0.2. v u u 1 σw (f, x, y) = t 2 (f (x + i, y + j) µw (f, x, y))2. w i= j= (3) To impove Niblack s method, Sauvola and Pietikainen [3] poposed a modified theshold TSau,w (x, y) which is expessed as ( ( )) σw (f, x, y) TSau,w (x, y) = µw (f, x, y) 1 k 1 R (4) whee both R and k ae set to 0.5 in [3]. Paametes k and k used in Eq. (1) and Eq. (4), espectively, ae sensitive to the contents of the input document images and may not be applicable fo degaded document images. Fo example, fo a degaded document image in Figue 2, Figue 2 and (c) ae binaized images obtained by Sauvola and Pietikainen s method with k = 0.01 and k = 0.2, espectively. The binaized image with smalle k peseves moe detailed contents but suffes fom moe noises. This obsevation motivates using two thesholding schemes to poduce two binaized images fom which the final binaized image is deliveed. 3. THE PROPOSED TWO-STAGE AND PARAMETER-FREE BINARIZATION METHOD In this section, we pesent a two-stage and paamete-fee binaization scheme fo degaded document images. The fist stage detemines a pope window size by consideing the vaiation of foegound pixel intensities within windows. In the second stage, based on the window size detemined in stage 1, a final binaized image is deliveed by contasting two binaized images poduced by two adaptive thesholding schemes which conside the content pese-

3 IR(w) Window size w IR(w) Window size w Figue 3: IR(w) fo documents in Figue 1 and (d) vation and noise suppessing. 3.1 Detemine the pope window size To stat the two-stage binaization scheme, we fist apply the Gaussian low-pass filte to obtain the smoothed image and then the Otsu s method is used to detemine the set of the ough foegound pixels, denoted by RF G. The vaiation of foegound pixel intensities within each window usually inceases as the window size inceases. Lage window size usually delives binaized images with bette quality but suffes fom lage computational cost, indicating that the window size lage than necessay fo acceptable quality should not be adopted. Since binaizing with small window size may eoneously binaize foegound pixels to backgound pixels and using lage window size inceases the computational cost without significantly inceasing the quality, we stat with a small window size and keep inceasing the window size until no substantial incease in the vaiation of the pixel intensities within each window is obseved. Stating with a window of size 3 3, we compute the standad deviation of the foegound pixel intensities within each window and use the aveage of the standad deviations as the indicato to seach fo the pope window size. Let IR(w) denote the inceasing ate of the aveage standad deviation when enlaging the window fom w w to (w + 2) (w + 2) and is expessed as with σ w = IR(w) = σ w+2 σ w σ w (5) 1 RF G (x,y) RF G σ w (f, x, y), (6) whee RF G is the cadinality of the set of ough foegound pixels RF G and σ w (f, x, y) is the standad deviation of pixel intensities within the w w window centeed at (x, y). The inceasing ate IR(w) deceases as the window size w inceases as shown in Figue 3. The pope window size w is the smallest window size such that IR(w) is less than o equal to 0.01; that is, w = min{w : IR(w) 0.01}. 3.2 Poposed noise-suppessing thesholding scheme Fo low contasting documents, infomation contained in the neighbohood of a specific pixel can be helpful in detemining the binaization theshold. Let g(x, y) denote the gadient magnitude, poposed by Sobel opeato [9], fomed fom the pixels in the neighbohood of pixel (x, y). Lage values of g(x, y) indicate that pixel (x, y) is aound the bounday between foegound and backgound pixels. Based on the window size w = detemined in stage 1, compute and = µ w (g, x, y) = 1 w 2 σ w (g, x, y) 1 w i= i= j= g(x + i, y + j), (7) j= (g(x + i, y + j) µ w (g, x, y)) 2.(8) When incopoating the infomation contained in the mean µ w (g, x, y) and the standad deviation σ w (g, x, y) of local gadients aound pixel (x, y), we popose a binaization theshold T (x, y) = µ w (f, x, y) ( ( ) 1 k e (µ w (g,x,y)+σ w (g,x,y))/m ), (9) whee M = max (x,y) D {µ w (g, x, y) + σ w (g, x, y)} with D denoting the input document. Deceasing paamete k inceases the theshold T (x, y) and the numbe of pixels identified as foegound pixels inceases. Thus when deceasing paamete k fom some lage initial value, the numbe of identified foegound pixels inceases shaply and becomes satuated as most of the tue foegound pixels ae coectly identified as foegound pixels. If keep deceasing the paamete k, the numbe of identified foegound pixels may incease shaply again since the pesent noises ae eoneously identified as foegound pixels. Let F G (k ) denote the numbe of identified foegound pixels using theshold T (x, y) on pixel f(x, y). Stating with k 0 = 0.2, iteatively decease the theshold by modifying the paamete k accoding to k i+1 = (0.9)k i. Denote by k 1 and k 2, with k 1 > k 2, the eflection points of the function F G (k d ); that is, 2 F G (k ) k = dk 2 =k 1 d 2 F G (k ) k = 0. Two thesholds T dk 2 =k 1(x, y) and T 2(x, y) 2 fo pixel (x, y) ae detemined by Eq. 9. Let B i denote the binaized image poduced by the theshold T i (x, y), i = 1, 2. Since T 1 (x, y) < T 2 (x, y), in image B 1 noises ae suppessed but some tue foegound pixels ae not coectly identified. On the othe hand, in image B 2 almost all the tue foegound pixels ae identified but in the meantime the noises ae about to be included. Two binaized images B 1 and B 2 ae then contasted to delive the final binaized image. Since T 1 (x, y) < T 2(x, y), if (x, y) is a backgound pixel in B 2, then (x, y) must be a backgound pixel in B 1 and is vey likely to be a tue backgound pixel in the document. Thus the pixel appeaed to be a backgound pixel in B 2 will be identified as a backgound pixel in the final binaized image. Similaly, if (x, y) is a foegound pixel in B 1, then (x, y) must be a foegound pixel in B 2 and is vey likely to be a tue foegound pixel in the document. Thus the pixel appeaed to be a foegound pixel in B 1 will be identified as a foegound pixel in the final binaized image. If (x, y) is a backgound pixel in B 1 and a foegound pixel in B 2, then it can be a foegound o a noise in the document. To tackle such pixels, a egion-gowing scheme, based on the pixels which ae identified as foegound pixels in both B 1 and B 2, is poposed. Fo each pixel (x, y) identified as a foegound pixel in both B 1 and B 2 images, a 3 3 window centeed at (x, y) is consideed. Within the window, fo each of the eight pixels suounding (x, y), if it is identified as a backgound in B 1 and a foegound in B 2, then it is identified as a foegound pixel in the final binaized image. Futhemoe, the egion-gowing scheme will be applied to the newly identified foegound pixel by the egion-gowing scheme. This poposed egion-gowing scheme mends some tue foegound pixels that ae suppessed in B 1 when suppessing the noises.

4 Table 2: The pefomance compaison of test image 3 Test image 1 (#F G/N = 11.03%) Test image 2 (#F G/N = 13.12%) Method [3] [4] [5] [7] [8] Poposed Pecision Recall Accuacy F-measue Figue 4: The test images when compaed to the existing methods. Table 1: The pefomance compaison of test image 1 Method [3] [4] [5] [7] [8] Poposed 4. Pecision Recall Accuacy F-measue EXPERIMENTAL RESULTS In this section, we empiically compae the poposed method with six existing methods Sauvola and Pietikainen s method [3], Zhao et al. s method [4], Gatos et al. s method [5], Chen et al. s method [7], and Moghaddam and Cheiet s method [8]. All methods ae implemented by Boland C++ Builde 6.0 and un on a standad PC with AMD Athlon 64X CPU(2.5 GHz) and 1.87 GB of RAM. The test images include scanned machine-pinted image and bluepint image fo which we ceate the tue binay image by human eyes. Test images 1 in Figue 4 is the bluepint image of achitectues with popotion #F G/N of foegound pixels, whee #F G is the numbe of tue foegound pixels in the document with N pixels. Test images 2 in Figue 4 is a textual image with non-unifom illumination. The pefomance evaluations ae based on fou accuacy measues: ecall, pecision, (c) accuacy, and (d) F-measue. Recall is the popotion of coectly binaized foegound pixels within the tue foegound pixels. Pecision is the popotion of tue foegound pixels within the binaized foegound pixels. Accuacy is the weighted aveage of the popotions of coectly binaized foegound and backgound pixels within the tue coesponding pixels with weights popotional to the numbes of tue foegound and backgound pixels. The F-measue is the hamonic mean of ecallz and pecision. Let T P and T N denote espectively the numbe of pixels that ae coectly binaized as foegound and backgound pixels. And denote espectively by F P and F N the numbe of pixels that ae eoneously binaized as foegound and backgound pixels. Then we have ecall = T P/(T P + F N ), pecision = T P/(T P +F P ), accuacy = (T P +T N )/(N ), and F-measue = 2 ecall pecision/(ecall + pecision). Empiical esults ae listed in Tables 1 and 2 espectively. Based on the empiical esults, the following geneal conclusions ae obvious: 1. The poposed method has significantly highe F-measue than the existing methods, indicating that the poposed method achieves highe accuacy in both ecall and pecision. 2. In tems of accuacy, the poposed method is competitive 3. Fo highly degaded documents such as test bluepint image 1, esults in Tables 1 show that the poposed method achieves highe pecision with acceptable ecall compaed to the existing methods. 5. CONCLUSION In this pape, a two-stage paamete-fee window-based binaization method is poposed. In geneal, the poposed binaization scheme is competitive when compaed with the existing methods. Specifically, the poposed method has good pefomance in both ecall and pecision measues, esulting a highe F-measue. 6. ACKNOWLEDGEMENTS K.-L. Chung, Y.-H. Huang, and W.-N. Yang ae suppoted by the National Science Council of R.O.C. unde Contact NSC E MY3, NSC E , and NSC E espectively. 7. REFERENCES [1] N. Otsu, A theshold selection method fom gay-level histogams, IEEE Tanscations on Systems, Man and Cybenetics, vol. 9, no. 1, pp , [2] W. Niblack, An intoduction to digital image pocessing, Pentice Hall, Englewood Cliffs, NJ, pp , [3] J. Sauvola and M. Pietikainen, Adaptive document image binaization, Patten Recognition, vol. 33, no. 2, pp , [4] M. Zhao, Y. Yang and H. Yan, An adaptive thesholding method fo binaization of bluepint images, Patten Recognition Lette, vol. 21, no. 10, pp , [5] B. Gatos, I. Patikakis and S. J. Peantonis, Adaptive degaded document image binaization, Patten Recognition, vol. 39, no. 3, pp , [6] J. Canny, A computational appoach to edge detection, IEEE Tanscations on Patten Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp , [7] Q. Chen, Q. S. Sun, P. A. Heng and D. S. Xia, A doubletheshold image binaization method based on edge detecto, Patten Recognition, vol. 41, no. 4, pp , [8] R. F. Moghaddam and M. Cheiet, A multi-scale famewok fo adaptive binaization of degaded document images, Patten Recognition, vol. 43, no. 6, pp , [9] R. C. Gonzalez and R. E. Woods, Digital Image Pocessing, Section 7:1.3: Edge Detection, Addison Wesley, 1992.

5 ABOUT THE AUTHORS Yung-Hsiang Chiu is now a Ph.D. student in the Depatment of Compute Science and Infomation Engineeing of National Taiwan Univesity of Science and Technology, Taiwan. His eseach inteests include document image pocessing and advance video coding. His contact is D @mail.ntust.edu.tw. Kuo-Liang Chung is a Chai Pofesso in the Depatment of Compute Science and Infomation Engineeing of the National Taiwan Univesity of Science and Technology, Taiwan. His cuent eseach inteests include image pocessing, video coding, and data hiding. His contact is k.l.chung@mail.ntust.edu.tw. Yong-Huai Huang is an assistant pofesso in the Institute of Compute and Communication Engineeing at Jinwen Univesity of Science and Technology, Taiwan. His eseach inteests include image pocessing and compession, and algoithms. His contact is yonghuai@ms28.hinet.net. Wei-Ning Yang is now an associate pofesso in the Depatment of Infomation Management, National Taiwan Univesity of Science and Technology, Taiwan. His eseach inteests include statistical analysis, stochastic simulation, and image pocessing. His contact is yang@cs.ntust.edu.tw. Chi-Huang Liao is now the manage at System Online Co. Ltd., Taiwan. His eseach inteests include geogaphic infomation system, system integation, and image pocessing. His contact is chin.laiw@msa.hinet.net.