Segmentation of Touching Telugu Characters under Noisy Environment 1

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1 Segmentation of Touching Telugu Characters under Noisy Environment 1 Srinivasa Rao A V, 2 Mary Junitha M, 3 Shankara Bhaskara Rao G, 4 Subba Rao A V 1 Research Scholar, Department of ECE, JNTU, Kakinada, AP, India 2 Assoc. Prof., Department of ECE, NRIIT, Guntur, AP, India 3 Assoc. Prof., Department of ECE, SVEC, Tadepalligudem,AP, India 4 Assoc. Prof., Department of ECE, St Mary group of Institutions, Guntur, AP, India ABSTRACT Recognition of Indian language scripts is a challenging problem. Work for the development of complete OCR systems for Indian language scripts is still in infancy. Development of OCR systems has been completed for Devanagari and Bangla scripts in the recent past. Research in the field of recognition of Telugu script faces major problems mainly related to the touching and overlapping of characters. In this, paper we propose a testing mechanism for segmenting touching Telugu characters by using the drop fall algorithm. Initially the documents are added with different noises at different levels. Then these documents are cleaned by Modified IGT algorithm. Keywords: Document Image Analysis, Document Cleaning, Segmentation, Different Noises, Noise Levels. 1. INTRODUCTION India is a multilingual country with a large number of written scripts. OCR development is yet to take a commercial shape for many of these scripts. Character segmentation is the first step of OCR system that seeks to decompose a document image into a sequence of sub images of individual character symbols. Segmentation of touching Telugu characters in the noisy environment is a highly difficult task. Due to the presence of noise the actual value of the pixel compared to the neighboring pixels has greater value. The removal of Salt Pepper noise from an image is a difficult task because it is ununiformly distributed in the image. Salt and pepper noise creeps into images in situations where quick transients take place. In such cases the segmentation is highly difficult, so we need a mechanism for removing noise. For removing this type of noise we use different type and order of filters. But in this work we are using adaptive method which developed for cleaning the ancient document images for digitizing. In general all documents are damaged by light, particularly ultraviolet light which is present in daylight. Frequent handling results in the steady physical wear and tear of the original, possibly resulting in eventual loss of the document. In addition, the documents are vulnerable to damage caused by fluctuating environments and light. So Binarization plays an important role in the image document analysis for removing the background noise and improving the readability of the document. 1.1 Binarization There are two Binarization techniques which are global threshold and local threshold. The Global threshold defines global value for all the pixels of the image in order to separate them as text or background [1]. This method is not able to remove noise that is not uniformly distributed in the image. On the other hand, local threshold provides an adaptive solution for the images with different background intensities, so that the threshold value varies according to the properties of all image area [11,12]. There are many general purposes. Binarization methods are capable of dealing with any document image which has complex backgrounds. These methods are completely based on the local or Adaptive threshold values. Srinivasa Rao et al. calculated the mean value based on the background information through iterations [14], Bensons calculated local threshold using the neighbours[2], Niblack calculated the threshold at each pixel using local mean and Standard deviation[3], Sauvola applied two algorithms in order to calculate a different threshold for each pixel [7]. 1.2 Segmentation Segmentation plays a mojor role in the document image analysis for identifying the individual ones after the binarization. Binarization is the pre-processing step for separating the foreground from the background in any document. Segmentation methods are described in [4,6]. A segmentation technique for touching of type printed Thai characters is proposed [10] by Sarin Watcharabutsarakham, which uses structural characteristics to detect suitable segmentation points in both horizontal and vertical directions. A new machine printed Arabic character segmentation algorithm is proposed [9] by Liying Zheng, based on vertical histogram with the rules are based on the structural characteristics between background regions as well as character components. The characteristics of isolate d Arabic characters are used to check whether the sub-word includes only one character. The vertical histogram associated with other rules used to find real segmentation points. Recognition of text heavily depends on proper segmentation of text into lines, words and then individual characters or sub-characters for feature extraction and classification of these characters. An error in segmentation may lead to wrong recognition of text and the system may be rendered useless. A detailed survey on Indic script recognition is presented in [8]. In some of North Indian script alphabets like Bangla, Gurmukhi, etc., 698

2 it is noted that many characters have a horizontal line at the upper part. It is called, head-line or sirorekha. A new approach to segmentation of machine printed Gurmukhi text is proposed [13] in literature using a two-pass mechanism. In pass-one it approximates the segmentation point, while in pass-two the cutting point is optimized. This approach is tested to be successful in segmenting a pair as well as triplets of touching characters. Congedo et al.[5] proposed drop fall algorithm attempt to build a segmentation path by mimicking an object falling or rolling in between the two characters which make up a connected component. There are four primary types of drop-fall algorithms which differ on the direction and the starting point of the drop fall. A new approach to segment printed ancient Telugu text is proposed [15]. In this paper we present an approach for segmenting the touching character by using drop fall algorithm under noisy environment, after cleaning the images by using adaptive technique. 2. TELUGU SCRIPT India is a multilingual country. Out of 18 officially recognized languages in India, 9 languages have separate scripts and the other languages are written either in Perso-Arabic script or Devanagari script..telugu is the official language of the state of Andhra Pradesh in southeastern India where it is spoken by close to 120 million people. The script consists of vowels, consonants, consonant-vowel core formation and a large number of conjunct formations. Vowels are 16 independent letters represented with individual glyph. Consonants are 35 individual letters with distinct glyph set. The dependent vowel signs also called matras play an important role in the formation of the glyph. Logically the character glyph formations for these combinations are METHODOLOGY A novel technique (flow chart of as Fig-1) for the cleaning and segmentation of Telugu text documents by adding different noises with varies levels the corresponding stages of phased way of processing is described. The detailed information of the present model is also illustrated in Fig-1 In the first phase of methodology, Cleaning is performed on Telugu text documents after adding different levels of varies noises. In the second phase Segmentation plays a major role in document image analysis. Segmentation of Telugu script into meaningful units is somewhat difficult because of cursive nature of the script. In this connection, segmentation of noisy document into syllables, still a challenging job till today. Fig 1: Flow Chart of the Proposed Model 3.1 Modified Igt Algorithm The series of sequential steps are necessary for the Modified IGT algorithm consists of various steps suitable for noisy documents are viz., Extraction of degraded(noisy) document; Conversion of noisy document into Gray-scale image; Average intensity of background+object; Intensity shifting of pixels in the image towards background; Equalize the image based on the influence of foreground object intensity on background information; Determine the average intensity of the resultant image; Evaluate the threshold between the iterative average intensities. A gray-scale image of the noisy document is generally represented by I(x,y) I(x,y)= S, S єs [0,1] (1) Where x and y are the horizontal and vertical coordinates of the image I(x,y), and S can take any value between 0 and 1 where S=1 stands for white and S=0 stands for black. In the proposed algorithm contain intermediate tones are shifted to background. In general the fact is any document image includes few pixels of useful information (foreground) compared to the size of the image (foreground+ background). Rarely the amount of object information exceeds 10% of the total pixels in the document. Taking this advantage, it was assumed that the average value of the pixels will be determined mainly by the background even if the document is quite clear. There are two parts in the proposed Modified IGT. In the first part the level shifting of the pixels of an image is evaluated, while the second part of the algorithm, 699

3 determines the relative importance of pixels with respect to object information. After each iteration some amount of pixels will be moved from fuzzy region to background. The iteration process will continue as long as the following criterion is satisfied is expressed by the Eqs-2 T T 1 t (2) i i where T i is the threshold used in ith iteration and T i 1 is the threshold before the i th iteration t is the sensitivity parameter of threshold 3.2 Drop Fall Algorithm Drop fall algorithms are based on the principle that a fairly optimal cut between two connected characters can be made if one were to role a hypothetical marble off the top of the first character and make the cut where the marble falls. Despite its apparent simplicity, the algorithm has proven itself to be quite useful. There are several methods available to decide where to start the drop-falling process from. Obviously, it is best to start as close as possible to the point at which the two characters are connected. Dimauro et al [13] outline a method which does this quite robustly. In this method, the pixels are scanned row-by-row until a black boundary pixel with another black boundary pixel to the right of it is detected, where the two pixels are separated by only white space. This pixel is then used as the point from which to start the drop fall. By scanning row-by-row, left to right, this is the first pixel which would meet the criterion of being a border pixel separated from another pixel to the right only by white space. A more naive choice for the initial pixel would be the first pixel found by scanning row-by-row which has white space to the right of it. This method fails, however, in when the first such pixel encountered is part of the second of the two connected characters. In this case, the algorithm will fall off the right side of the characters After the initial pixel is found, the next step is to begin the actual drop fall. The drop-falling algorithm is designed to mimic falling, so it will always move downwards, diagonally down-wards, to the right, or two the left. The directions that the algorithm will move are according to the current pixel position and its surroundings. 4. EXPERIMENTAL RESULTS The proposed algorithms are tested on different Telugu touching characters at different noise levels of various noise sources. These samples are created by using the paint brush based on the regular observation of different newspapers. Here we are given one touching word AATHHA at different noise level of Gaussian, Salt & Pepper, Speckle and Poison Noise is presented in Tables 1,2,3,4. In the case of Gaussian noise the noise distribution is symmetric over the image. So MIGT algorithm is better suited to documents contaminated with Gaussian noise. Segmentation accuracy with the proposed hybrid algorithm is low at high noise density levels are presented in Table 1. Segmentation accuracy is very high in the case of Speckle and Possions noise due to the uniform distribution of the noise in the image. In the case of Salt & Pepper the noise is randomly distributed over the image. Concentration of noise in some areas of the image is denser than the rest of the part. So it is difficult to clean by using MIGT algorithm. The resultant images contain some noise even though images are cleaned by MGIT algorithm is presented in Table 2. Segmentation accuracy is very poor at the middle values of noise levels. Table 1: Gaussian Noise 700

4 Table 2: Salt & Pepper Noise Table 4: Poisson Noise These Tables shows the effectiveness of the algorithms for different noise sources. In this regard we are trying to give an analysis for segmenting the touching characters. The same analysis can be used for any language under certain conditions imposed 5. CONCLUSIONS The proposed algorithm is tested on set of touching characters that are regularly occurred in the hand written documents in offices, schools etc., Here we are given one sample which is generated by paint brush. Segmentation analysis is made based on the different noise intensities. There is chance to segment the touching characters in the noisy environment. Segmentation accuracy is dependent on the noise level and characteristics of noise. The same work is extended to segment the touching hand written characters under noisy environment. Table 3: Speckle Noise REFERENCES [1] N.Otsu, A threshold selection method from a gray level histograms, IEEE Trans.Systems, Man, Cybernet., 9(1),1979, pp [2] J.Berson, Dynamic thresholding of gray-level images, 8 th Int. Conf. on pattern recognition, 1986, pp [3] W. Niblack An Introduction to Digital Image Processing, Prentice Hall, 1986, pp [4] Liang, S., Sridhar. M. and Ahmadi., Segmentation of Touching Characters in Printed Document Recognition, Pattern Recognition, 1994, vol.27, no.6, pp [5] G.Condego, G.Dimauro, S.Impedovo and G.Pirlo., Segmentation of Nuemaric Strings Proc. Of Third Int. Conf. on Document Analysis and Recognition, Montreal, Aug 14-16, 1995, pp [6] Casy, R.G. and Lecolinet. E., A Survey of Methods and Strategies in Character Segmentation IEEE Trasactions on Patterns Analysis and Machine Intellegence, 1996, vol.18, no.8, pp [7] J.Sauvola, M.Pietikainen, Adaptive Document Image Binarization, Pattern Recognition, 33, 2000,pp

5 [8] U.Pal and B.B.Chaudhari, Indian Script Character recognition: A Survey, Pattern Recognition, 37(2004), pp [9] Liying Zheng, Abbas H. Hassin a, Xianglong Tang, A new algorithm for machine printed Arabic character segmentation Pattern Recognition Letters vol.25, (2004), pp [10] SarinWatcharabutsarakham., SegmentationforTouc hing Typewrittens, TENCON IEEE Region 10 Conference Volume A, Issue, 21-24, Vol. 1, Nov Page(s): M Mary Junitha obtained his B.Tech degree in Electronics and Communication Engineering from Karnataka University, Karnataka, India. MS from BITS Pilani, Rajasthan and M.Tech in from JNT University, Hyderabad, AP, India. She is presently working as Associate Professor in NRI Institute of Technology, Guntur, Andhra Pradesh, India. She has 17 years of teaching experience. Her area of interests include Image Processing and Radar. [11] E.Kavallieratou, ABinarization Algorithm Specialized on document images and photos, 8 th Int. Conf. on Document Analysis and Recognition, 2005, pp [12] B.Gatos, I. Pratikakis, and S.J.Perantoni, Adaptive degraded document image binarization, Pattern recognition, vol.39,pp ,2006 [13] Utpal. Garain and Bidyut B. Chudhury., Segmentation of Touching Characters in Printed Devanagari and Bangla Scripts Using Fuzzy Multifactorial Analysis, IEEE Transactions on Systems, Man and [14] A.V.Srinivasa Rao, N.Venkata Rao, S.Babji, L.Pratap Reddy Cleaning of Ancient Document Images Using Modified Iterative Global Threshold Proceedings of International Journal of Computer Science Issues November,2011 [15] Srinivasa Rao A V., Segmentation of Ancient Telugu Text Documents., IJIGSP, 2012, 6, AUTHOR PROFILES Adabala Venkata Srinivasa Rao obtained his B.Tech degree in Electronics and Communication Engineering from JNT University, Kakinada, AP,India. AMIE Electrical from Institute of Engineers (India), Kolkotta, India. and M.Tech in Instrumentation and Control Systems from JNT University, Kakinada, India. He is presently working as Research Scholar in JNTU, Kakinada, Andhra Pradesh, India. He has 10 years of teaching and 4 years of industrial experience. He has 16 publications in various National, International Conferences and journals. His area of interests includes Pattern Recognition, Image Processing and VLSI. He is an active member in professional bodies like AMIE, IACSIT G Shankara Bhaskara Rao obtained his B.Tech degree in Electronics and Communication Engineering from JNT University, Hyderabad, AP,India. and M.Tech in Communication Systems. He is presently working as Associate Professor in Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India. He has 10 years of teaching experience. His area of interests include Pattern Recognition, Image Processing and VLSI. A V Subbarao obtained his B.Tech degree in Electronics and Communication Engineering from JNT University, Kakinada, AP,India. and M.Tech in Instrumentation and Control Systems from JNT University, Kakinada, India. He is presently working as Associate Professor in St.Mary Group of Institutions, Guntur Andhra Pradesh, India. He has 11 years of teaching experience. He has 2 publications in various National, International Conferences and journals. His area of interests include, Image Processing and VLSI. 702