Evaluation of several binarization techniques for old Arabic documents images

Transcription

1 Evaluation of several binarization techniques for old Arabic documents images Abderrahmane KEFALI (1), Toufik SARI (2), Mokhtar SELLAMI (1) (1) Laboratoire de Recherche en Informatique (LRI), Département Informatique, BP 12 - Université Badji Mokhtar - Annaba, 23000, Algérie (2) Laboratoire de Gestion Electronique de Documents (LabGed), Département Informatique, BP 12 - Université Badji Mokhtar - Annaba, 23000, Algérie {abderahmane.kefali, sellami}@lri-annaba.net sari@labged.net Abstract Binarization is an important stage in all process of images processing and analysis. Currently, lot of techniques were proposed in the literature for binarizing colored and gray-level images, of which each one is appropriate to a particular type of images, but unfortunately no one among them surpassed in the case of the binarisation of old manuscript documents. These lasts are characterized by their bad quality due to the various deteriorations suffered in the life cycle of documents and to materials used for their storage. This paper presents an evaluation of binarization techniques frequently cited in the literature. In the first stage we studied, implemented and tested twelve binarisation algorithms over old Arabic manuscripts document. We however tried to compare them by using a new method based on features extracted from each image. The goal of our work is in a middle term to do a comparative study and can choose thereafter the best binarisation algorithm to use it in our Arabic documents retrieval system. Key words: thresholding, binarization, old Arabic documents. 1. Introduction Binarization is often the first stage in all systems of images (and more particularly documents images) processing and analysis [1][2]. We can cite here: Optical character Recognition (OCR), word spotting and indexation. It has as goal to decrease the quantity of information present in the image and to guard only relevant data, which allows as to use simple analysis methods face to face of gray-level or colored images [3]. Performances of the following steps in the documents analysis systems depends highly on the results of the used binarization algorithm, it is wherefore the binarization step must be performed the most accurately possible. It must on the one hand; conserve the maximum of information and details present in the entered image, and in the other hand it must eliminate the noise superposed to the original data. In fact, the problem of documents binarization is older than documents images analysis itself. For that, a very important number of binarization techniques were proposed in the literature which each one between them is appropriate to a particular type of images. Indeed, a lot of techniques were proposed in the two lasts decades for binarizing gray-level documents images, others are applicable directly over colored images [4], but unfortunately, no one between them showed powerful for the binarization of degraded old documents, characterized by a complex structure, illumination variety, presence of stains and holes, transparence effect etc. We find in the literature several works interested by the comparison of binarization methods over several types of images, citing for example [1] [7] [21] [22] [25]. In this paper, we tackle with the binarization of old Arabic documents images. For that, we implement twelve binarization algorithms, the most frequently cited in the literature, and we apply them over a set of degraded old Arabic documents images in order to evaluate their performances. For the evaluation, we use a new method consists to compare features extracted from images instead of the comparison between these images. We interest in this study by the binarization of gray-level documents only, because, the most of colored documents may be converted easily and faithfully to gray-level. The rest of this paper is organized as follow: in section 2, we cite the families of binarization methods and we give a state of the art of the studied binarization methods. Afterwards in the section 3, we present our test collection composed of old Arabic documents images, the obtained results, and the evaluation of these results. Finally, the conclusion will be in the section State of the art

2 According to several research works [5][6], the binarization techniques gray-level images can be classified in two categories: global thresholding where one threshold is used in the whole image to separate it in two classes (text and background), and local thresholding where the threshold values are determined locally, pixel by pixel or region by region. Other [3], added a third group of hybrid methods. These methods combine global and local information for assign the pixels to one of two classes (text or background). In [7], the binarisation methods were classified according to the information that they exploit in six categories: methods based on histogram shape, clustering, entropy, object attributes, spatial methods and finally local methods. In this section, we consider the first classification (global and local methods) and we present a little state of the art of several binarization methods the most frequently cited in the literature. For global thresholding methods, we choose: fixed global thresholding [8], Otsu method [9], ISODATA [10], Kapur [11], Cheng and Chen [12], Li-Lee [13], with a global version of the Nick method [6]. And for local methods, we choose the Bernsen method [14], Niblack [15], Sauvola [16], Wolf [17], Nick [6] and the Fuzzy hierarchical segmentation of Gadi and Benslimane [18] Global methods Fixed global thresholding It is the most simple binarization technique [8]; it consists to compare the gray-level of each pixel x i of the image with a global threshold T (for example 127). We note b i the new value of the pixel, thresholding is given by the following expression: b i = 255 if x i T and b i = 0 if x i < T Otsu method Otsu method [9] is one of the famous global methods. This method tries to find the threshold T which separates the gray-level histogram in an optimal way into two segments (which maximize the inter-segments variance or which minimize the intra-segments variance). We note here that the calculation of inter-classes or intra-classes variances is based on the normalized histogram of the image H= [h 0...h 255 ] where h i =1. The inter-classes variance for each pixel t is given by: σ inter 2 = q 1 (t) q 2 (t) [µ 1 (t)-µ 2 (t)] 2 Where: μ 1 t = 1 q 1 t q 1 t = t 1 i=0 h i i and μ 2 t = 1 q 2 t t 1 i=0 h(i) and q 2 t = 255 i=t 255 i=t h(i) h i i The limit of this method is that it applicable only when the image is bimodal (the histogram contain two peaks). Wolf and Jolion [19] proposed to use a two-thresholds version of the Otsu method for binarizing video images, where the first threshold k haut correspond to the threshold calculated by the Otsu method, and the second threshold k bas is calculated from the threshold k haut and the first mode of the histogram: k bas = m (k haut - m 0 ). Thus deciding the membership of pixels to one of Object or background classes is modified as follow: I x,y < k bas B x,y = 0 I x,y > k haut B x,y = 255 k bas > I x,y > k haut B x,y = 255 if exist a way to a pixel I u,v > k haut 0 else

3 Such as I x,y is the gray-level of the pixel (x, y), and B x,y is its value after binarisation ISODATA method Thresholding using ISIDATA [10] consists to find a threshold by separating iteratively the gray-level histogram into two classes, with the apriority knowledge of the values associate to each class. This method starts by dividing the interval of non-null values into two equidistant parts, and next we take m 1 and m 2 as the arithmetic means of each class. Repeat until convergence, the calculation of the threshold T as the closest integer to (m 1 +m 2 )/2 and update the two means m 1 and m Kapur method The kapur method [11] is considered as an extension of the Pun method [20]. In contrast to this last, the Kapur method takes in the account the object likelihood distribution P t and the background likelihood distribution (1- P t ) in the determination of the division entropy. The binarisation threshold T is chosen of which the value: H=H object +H background be maximal. T is given thus by the expression: T = max (H object + H background ) = max ( t P i P t i=0 log P i P t 255 i=t+1 P i 1 P t log P i 1 P t ) Such as P i is the likelihood of occurrence of the gray-level i in the image, and P t = t i=0 P i Cheng-Chen method The Cheng-Chen method [12] is based on the maximal entropy principle and the fizzy C-partition, for the calculation of the threshold. Consider two fuzzy sets Object and Background of which the membership functions are defined by: 1, x a µ object = (x-c)/ (a-c) a<x<c 0, x c 0, x a µ background = (x-a)/(c-a) a<x<c 1, x c In this method, the binarization threshold is chosen as the gray-level of which the membership function=0.5, and so it is the center of the interval [a opt, c opt ], such as a opt and c opt are the values of a and c maximizing the division entropy. Cheng and Chen proposed an algorithm for finding the optimal values of a and c corresponding to the maximal partitioning entropy. The partitioning entropy is given by: H= -P(object) log (P(object))-P(background) log (P(background)) Li-Lee method Li and Lee [13] proposed an images thresholding technique where the grouping of gray-levels in two classes is based on the crossed entropy minimization. Note µ 1 (t) and µ 2 (t) the means of the two classes according to the gray-level t, and h i the histogram value at the gray-level i. Li and Lee established that the optimal threshold T is calculated in order to minimize η(t)= η 1 (t)+ η 2 (t) where: 2.2. Local methods η 1 t = t 1 i=1 i i h i log ( μ 1 (t) ) and η 2 t = μ 1 t = t 1 i h i i=1 t 1 i=1 h i et μ 2 t = 256 i=t i i h i log ( μ 2 (t) ) 256 i h i Bernsen method It is an adaptive local method of which the threshold is calculated for each pixel of the image [14]. Thus for each pixel of coordinates (x, y), the threshold is given by: i=t 256 i=t h i

4 T x, y = Z low + Z high 2 Such as Z low and Z high are the lowest and the highest gray-level respectively in a squared window r r centered over the pixel (x, y). However if the contrast measurement C(x, y)= (Z haut - Z bas ) is lower than a threshold l, then the neighborhood consists of a single class: background or text. In [22], the tests performed shows that the choice of l=15 and r=15gives the bests results Niblack method The Niblack algorithm [15] calculates a local threshold for each pixel by gliding a rectangular window over the whole image. The threshold T is calculated by using the mean m and the standard deviation σ of all pixels in the window (neighborhood of the pixel in question). Thus the threshold T is given by: T = m + k* σ Such as k is a parameter used for determining the number of edge pixels considered as object pixels, and takes a negative values (k is fixed -0.2 by authors). In [21], a window of size gives good results. There is in the literature several evaluation works of binarization methods of which several between them [22] [1], consider the Niblack method (together with a post-treatment step of Yanowitz and Bruckstein [23]) as the better Sauvola method The Sauvola algorithm [16] is a modification of that of Niblack, in order to gives more performance in the documents with a background containing a light texture or too variation and uneven illumination. In the Sauvola modification, the binarization is given by: T = m. (1 k. (1 σ R )) Where R is the dynamic carried of the standard deviation σ, and the parameter k takes positives values in the interval [0.2, 0.5]. In his tests, Sauvola used R=128 and k=0.5 According to [6], the Sauvola method is shown more effective that Niblack method when the gray-level of the text is close to 0 and that of background is close to 255. However in images where the gray-level of background and text pixels are closes, the results are unsatisfactory Wolf method In order to address to Sauvola algorithm problems (low contrast, disparity of gray-level, etc.), Wolf and al. [17] proposed to normalize the contrast and the image gray-level mean, and calculate the threshold by: T = 1 k m + k M + k σ (m M) R Such as k is fixed to 0.5, M is the minimum gray-level of the image and R is the maximum standard deviation of gray-levels obtained over all windows Nick method The major advantage of this method is that it improves considerably the binarization of white pages and lighted images and in the case where the image presents low contrast, by downwards moving, the threshold of binarization [6]. The threshold T is calculated as follow:

5 ( p 2 T = m + k i m 2 ) NP Such as: k is the Niblack factor and vary between -0.1 and -0.2 according to the application need, m: the average gray-level, p i : the gray-level of pixel i and NP is the total number of pixels. Kurshid and al assume that this method works very well for the most (if not all) type of degraded old documents. In order to evaluate their performances, they applied the method globally and locally over images documents. The window size in the case of local test is chosen equal to Fuzzy hierarchical segmentation It is an adaptive thresholding method based on hierarchical principle to palliate the non-uniform illumination problem presented in the most of real images [18]. This method consists of two steps: Hierarchy construction In this step we construct the quaternary tree, by decomposing consecutively the image into four sub-images by evaluating a homogeneity criterion. Each sub-image is associated to a node of the image. If one sub-image satisfied the homogeneity criterion, the division is not necessary, and the sub-image becomes a terminal node of the image. In the opposed case, this sub-image is decomposed into four parts. The process continues until all the nodes in the tree become terminals Decision and defuzzification For each hierarchical level, we calculate the membership degree of each pixel (m, n) to the Object and Background classes. We use for that, the Zadeh S function. The membership degree is given by: µ (m, n) = S (x mn ; m σ, m, m + σ) Such as: m and σ are respectively the mean and the standard deviation of the given sub-image gray-level and x mn is the gray-level of the pixel (m, n). Afterwards, we use the t-conorme Zadeh operator [24] to calculate the final membership degree of each pixel (m, n) to the Object (µ F o (m, n)) and Background classes (µ F f (m, n)). A pixel is affected thus to the Object class if its membership degree incorporated to the Object class is upper than that incorporated to the Background class. Else it is affected to the Background class. 3. Experimentation and results In this section, we present at first our test collection on which we applied the binarization methods above mentioned and next the obtained results Test images We collected a database of Arabic manuscript old images grace to different partnership projects realized by the two laboratories of our department that is the LRI and the LabGed. These document images are of different nature, having several characteristics coming from their natures, their storage state: degradation characteristics (stains, holes, humidity traces, ink degradation and thin lines, transparence effect, presence of folds and tears etc.), characteristics attached to documents nature ( the page may contain graphic areas of different sizes, low separation between text blocks, high variability between writing styles, irregular inter-words space, variations of paper color etc.), and characteristics of failing digitization ( of curvature, of light etc.) All these characteristics make difficult all type off processing and analysis of these images. Figure 1 shows some Arabic manuscript old images of our test collection having a high complexity degree.

6 Figure 1: Some examples of documents images of our test collection 3.2. Obtained results We applied the binarization methods previously presented over our test collection composed of 120 old Arabic documents images having several types of degradations and structure complexity. We present in this section the results of applying the previous methods over three images of our collection. For local methods, we tested several values of parameters k, R and the size r of the sliding window, and we choose those which give the best results. So for the Bernsen method we fixed r=15 15, for Niblack method, k = -0.2 and r=25 25, for Sauvola method, k= 0.2, R= 128 and r= For Wolf method the values k= 0.2 and r= 7 7 are chosen and for Nick method, k=-0.2 and r= Originals images Global methods Absolute S=127 Otsu

7 Otsu 2 thresholds ISODATA Kapur Cheng and Chen Li and Lee Nick k= -0.1

8 Hybrids methods Otsu ISODATA Locales methods (r designate the window size) Bernsen r=15 15 Niblack k= -0.2 r=25 25 Sauvola k= 0.2 R= 128 r= Wolf k= 0.2 r= 7 7

9 Nick k=-0.2 r=19 19 Gadi and Benslimane 3.3. Evaluation In several works, the evaluation of document images binarization methods is based on the calculation of the recognition rate of characters presents in the documents [22] [6] [21], by using generally a commercial OCR software as for example ABBYY Fine Reader. In other works, the evaluation is performed by calculating a distance (edit-distance for example) between the images resulting from binarization and Ground truth images [7] [3] [6]. Applying the OCR as an evaluation criterion, is not possible in our case, because the absence of an efficient commercial software for recognizing Arabic manuscript writing. And as Ground truth images are not available for our collection, using the distance with the images resulting from binarization is impossible too. As we previously said, the goal of our comparative study is to choose the best binarisation algorithm to use it in our Arabic documents retrieval system. We proposed thus to use this system in the comparison of binarisation methods. For that, we firstly assigned to each image of our test collection a textual signature (code) manually. This signature represents structural features of the writing present in the image (descenders, ascenders, loops and diacritics), and it is obtained by corresponding to each feature a precise code. We consider these signatures as Ground truth signatures. Over images resulting from the different binarization methods, we apply the series of processing proposed in our Arabic documents retrieval system [26] (smoothing, line and sub-word segmentation etc.) finishing by an extraction of structural features of characters presents in documents. Features extracted from each image are coded in ASCII and make up the signature of this image. For each image of our test collection, we compare the corresponding ground truth signature with signatures extracted from images binarized by using the different binarization methods, and we use for that the famous editdistance [27]. The best binarization algorithm for each image is thus that with which the obtained signature is the most close to the ground truth signature. The Table 1 recapitulates averages distances obtained between signatures extracted from images binarized by using the different binarization methods and the ground truth signatures.

10 Method Average distance Fixed global thresholding Otsu Otsu 2 seuils ISODATA 39 Kapur 64 Cheng and Chen 227 Li-Lee Global Nick Otsu hybride ISODATA hybride Bernsen Niblack 146 Sauvola Wolf Local Nick Fuzzy hierarchical segmentation Table 1: averages distances obtained for the different binarization methods From the precedent table, it is shown that the Nick method of Khirshid and al. [6] is the best for our test collection. 4. Conclusion In this paper, we tackled the general problem of binarizing degraded old Arabic documents. A lot of methods were proposed in the literature for binarizing documents images, but no one between them demarcate to the others, especially for the old documents images having different degradation characteristics. We presented a little state of the art over documents images thresholding techniques the most frequently cited in the literature. An implementation of these methods is performed by applying them over a set of 120 old Arabic documents images containing different types of degradations, and structure complexity. Because the absence of an efficient commercial software for recognizing Arabic manuscript writing, and the no- availability of ground truth images for our collection, we propose to use a new evaluation method. This last is based on the comparison of features extracted from images binarized by using the different binarization methods with ground truth features. The best binarization method is that with which the obtained features are the most closes to the ground truth features. The results showed that the Nick method is the best. 5. References [1] Ø. D. Trier and T. Taxt, «Evaluation of binarization methods for document images», IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), p , March [2] G. Leedham, S. Varma, A. Patankar, and V. Govindaraju, «Separating text and background in degraded documents images - A comparison of global thresholding techniques for multi-stage thresholding», Proceedings of the 8 th International Workshop on Frontiers in Handwriting Recognition, p , Canada, August [3] J. Sauvola, M. Pietikainen, «Adaptive document image binarization», Pattern Recognition, 33(2), p , [4] C. M. Tsai and H. J. Lee, «Binarization of color document images via luminance and saturation color features», IEEE Transactions on Image Processing, 11, p , April [5] N. Arica, F. T. Yarman-Vural, «An overview of character recognition focused on off-line handwriting», IEEE transactions on systems, man and cybernetics - part C : Applications and reviews, 31(2), p , [6] K. Khurshid, I. Siddiqi, C. Faure, N. Vincent, «Comparison of Niblack inspired Binarization methods for ancient documents», 16 th International conference on Document Recognition and Retrieval, USA, [7] M. Sezgin, B. Sankur, «Survey over image thresholding techniques and quantitative performance evaluation», Journal of Electronic Imaging, 13(1), p , January 2004.

11 [8] M. R. Gupta, N. P. Jacobson, E. K. Garcia, «OCR binarization and image pre-processing for searching historical documents», Pattern Recognition, 40, p , [9] N. OTSU, «A Threshold Selection Method from Gray-Level Histograms», IEEE transactions on Systems, Man and Cybernetics, 9(1), p , [10] F.R.D. Velasco, «Thresholding using the ISODATA clustering algorithm», IEEE Transaction on system, Man and Cybernitics, 10, p , [11] J.N. Kapur, P.K. Sahoo, A.K.C. Wong, «A New method for gray-level picture threshold using the entropy of the histogram», Computer Vision, Graphics, and Image Processing, 29, p , [12] H.D. Cheng, J. R. Chen and J. Li, «Threshold selection based on fuzzy c-partition entropy approach», Pattern Recognition, 31(7), p , [13] C.H. Li, C.K. Lee, «Minimum Cross Entropy Thresholding», Pattern Recognition, 26(4), p , [14] J. Bernsen, «Dynamic thresholding of grey-level images», in Proc. 8 th International Conference on Pattern Recognition, p , Paris, France, [15] W. Niblack, «An Introduction to Digital Image Processing», Ed. Prentice Hall, Englewood Cliffs, [16] J. Sauvola, T. Seppänen, S. Haapakoski, M. Pietikänen, «Adaptive document binarisation», Fourth International Conference Document Analysis and Recognition (ICDAR), p , Ulm, Germany, August [17] C. Wolf, J. M. Jolion, and F. Chassaing. «Extraction de texte dans des vidéos : le cas de la binarisation», In 13 ème Congrès Francophone de Reconnaissance des Formes et Intelligence Artificielle, p , [18] T. Gadi, R. Benslimane, «Segmentation hiérarchique floue», Traitement du signal, 7(1), [19] C. Wolf, J. M. Jolion, «Détection et extraction de texte de la vidéo», CFED, p , [20] T. Pun, «A New method for gray-level picture threshold using the entropy of the histogram», Signal processing, 2(3), p , [21] J. He, Q. D. M. Do, A. C. Downton, and J. H. Kim, «A comparison of binarization methods for historical archive documents», International Conference on Document Analysis and Recognition (ICDAR), p , [22] A.J.O. Trier, «Goal-directed evaluation of binarization methods», IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12), p , [23] S.D. Yanomitz and A.M. Bruckstein, «A new method for image segmentation», In Computer Vision, Graphics and Image Processing, 16(1), p , [24] L. A. Zadeh, «Fuzzy Sets and their Applications to cognitive and Decision Processes», p. 1-39, Ed. Academic Press, London, [25] L. O Gorman, «Experimental comparisons of binarization and multithresholding methods on document images», in: Proceedings of the IAPR International Conference on Pattern Recognition, vol. 2, IEEE, 1994, pp [26] T. Sari, A. Kefali, «A search engine for Arabic documents», Actes du dixième Colloque International Francophone sur l Écrit et le Document (CIFED), p , [27] Levenstein A., «Binary Codes Capable of Correcting Deletions, Insertions and Reversals». Sov.Phy. Dohl. Vol.10, P , 1966