Gujarati Character Recognition:Survey

Transcription

1 Gujarati Character Recognition:Survey Ms. Honey Patel Computer Engineering CHARUSAT,Changa Anand, India Ms. Rinku Bathani Computer Engineering CHARUSAT,Changa Anand, India Abstract Today most of people are e-reader. Gujarati documents are mostly in hardcopy; very less e-material is available. For converting those hardcopies into editable text form we need digitization. Optical Character Recognition is a technique for converting scanned documents into digitized text. So also for Gujarati language, optical character recognition system is required. For the recognition of printed characters of Indian languages like Gujarati, Marathi and many more; optical character recognition is required. Efforts of many researchers are going on for developing efficient OCR systems for Indian languages, especially for Gujarati, a popular language state Gujarat. There is very less material available for Gujarati script. Devnagari language is the base of Gujarati language. Only difference is shirorekha. Since 1999, efforts are going on for Gujarati character recognition but still that much accuracy cannot be obtained as compared to other Indic languages. Keywords Gujarati Character Recognition, Pre-processing, Segmentation, Feature Extraction, OCR I. INTRODUCTION Now a day, everything is going in the form of digital. Most of users are becoming e-users then all documents which are going to delivered them should be in digitized format. In recent years, the huge usage of internet had turned people to more usage to electronic documents compared to physical documents. The creation of electronic documents facilitate to easy communication and storage of documents. Thus, developing computer algorithms to identify the characters in the text is the principal task of OCR. Optical character recognition belongs to the family of techniques performing automatic identification. The overwhelming volume of paper-based data in corporations and offices challenges their ability to manage documents and records. Computers, working faster and more efficiently than human operators, can be used to perform many of the tasks required for efficient document and content management. Computers understand alphanumeric characters as ASCII code typed on a keyboard where each character or letter represents a recognizable code. However, computers cannot distinguish characters and words from scanned images of paper documents. Therefore, where alphanumeric information must be retrieved from scanned images such as commercial or government documents, tax returns, passport applications and.credit card applications, characters must first be converted to their ASCII equivalents before they can be recognized as readable text. Optical character recognition system (OCR) allows us to convert a document into electronic text, which we can edit and search etc. OCR is the machine replication of human reading and has been the subject of intensive research for more than five decades. A. Concept of Optical Character Recognition Normal way of entering data into computer is through key-board. This method is not so efficient and best. Some alternative automatic 459

2 process should be there. For converting printed or hand written scanned documents into digitized form optical character recognition is used. Text form generated at the end of OCR process can be edited; this is one of the advantages of using OCR. B. Types of OCR There are three types of OCR. 1. Offline Handwritten Text: In offline handwritten text, text is produced by a person by writing with a pen/pencil on a paper medium and which is then converted into digital format using scanner. 2. Online Handwritten Text: In online handwritten text, text is directly written in a digitizing tablet using stylus. The output is a sequence of x-y coordinates that express pen position as well as other information such as pressure and speed of writing. 3. Machine Printed Text: In daily use machine printed text is commonly found which is produced by offset processes such as laser printer, inkjet and many more. C. Introduction to Gujarati Script Gujarati script is derived from Devnagari script. It has very rich set of consonants and vowels. It has 34 consonants as shown in Table 1 and 12 vowels as in Table 2.. TABLE GUJARATI CONSONANTS ક ખ ગ ઘ ચ છ જ ઝ ટ ઠ ડ ઢ ણ ત થ દ ધ ન પ ફ બ ભ મ ય ર લ ળ વ શ ક ષ જ ઞ By combining consonants and vowels, compound characters are created. Compound characters are also known as conjuncts. Conjuncts are shown in Table 3. These conjuncts make Gujarati script more complex compared to other Latin language. Gujarati script is written from left to right. The basic difference between Devnagari script and Gujarati script is absence on shirorekha TABLE 2. GUJARATI VOWELS અ આ ઇ ઈ ઉ ઊ એ ઐ ઓ ઔ અ અ TABLE 3. EXAMPLE OF CONJUNCTS વ મ ત મ બ દ ભ મ ચ શ જ મ મય ડ ડ દ ધ હ ય ન ન શ વ D. Flow of Optical Character Recognition Scanning Pre-Processing Segmentation Post- Processing Digital Form Figure 1. Classification/ Recognition II. LITERATURE SURVEY A. Dataset Flow of OCR Feature Extraction For Gujarati script, there is no such unique dataset is available. For experimental purpose dataset is created as per requirement of author. Characters are cropped and stored in image form. Images are scanned on high-resolution for better result. For performing optical character recognition there are mainly below phases:

3 1. Pre-processing 2. Feature extraction 3. Classification and recognition 4. Post-processing B. Pre-processing For converting image into a suitable form for later processing steps, pre-processing is required. It makes image better for further processing. Typical preprocessing includes the following stages: 1. Binarization: Binarization converts the gray scale image into binary image. From the reported work [1], there are mainly two approaches for converting image into binary image. Binarization separates the foreground and background information. This separation of text from background is a prerequisite to subsequent operations such as segmentation. Global algorithm Local Algorithm Global algorithm calculates one threshold for entire image whereas Local algorithm calculates different threshold values depending on the local regions of image. Prior is faster compared to later method. The major drawback of global thresholding techniques is that it cannot differentiate those pixels which share the same gray level but do not belong to the same group. In comparison with global technique, local thresholding techniques have better performance against noise and error. Examples of global algorithm are Otsu s method, Histogram peaks, and K-mean clustering based method. Examples of local algorithm are Niblack s method, Sauvola s method, Bernsen s Method. The most commonly used method is Otsu s method [2]. 2. Noise removal: Due to printer, scanner, print quality, age of the document, the documents which are scanned may have noise. So, it is pre-requisite to filter this noise before processing the image. Commonly used approach is to process the image through a low-pass filter and use it for later processing. Median filter is best suitable for salt and pepper kind of noise. Maximum filter is used for removing negative outlier noise. Minimum filter is used for removing dark values in image [3]. 3. Skew detection and correction: Lines of text in the digital image make an angle with the horizontal direction is called skew angle. When document is put on the scanner, by human or machine error skew is generated. Techniques for skew detection and correction are Radon Transform, Hough Transform, Moments, nearest neighbor, Fast Fourier Transform, Projection profile analysis, Scan line method, Base point method [4-5]. Radon Transform is used for finding skew angle. Here radon transform calculate on image with 0 to 179 degree of rotation angle around the center of image [6]. The radon based skew estimation method is ideal for document images containing pictures. It can be applied to document images containing text as well. 4. Segmentation: An image which is passed from the preprocessing phase is ready for the segmentation process. Commonly used segmentation algorithms are connected component labeling, Projection profile, Run-length smearing, Hough transforms [7]. Segmentation contains three steps: Line Segmentation Word Segmentation Character Segmentation A. Line segmentation: Projection and Hough-based methods are suitable for clearly separated lines [7]. 461

4 Projection-based methods can cope with few overlapping or touching components. Projection-profiles are commonly used for printed document segmentation. The global horizontal projection is used to compute the sum of pixels in each row and construct corresponding histogram Find the rows which are not contain white pixel using histogram. Based on the peak/valley points of the histogram, individual lines are separated. As shown in Fig-2, each binarized line is separated. Figure 2. Example of Line Segmentation B. Word segmentation: For separate words from segmented line, line is scanned vertically. Number of black pixels in each column is calculated to construct column histogram. Vertical histogram is used for word segmentation [8]. Using the vertical Histogram, find the points from which the word starts and ends. Fig-3 shows the words segmented from Fig-2. Figure 3. Example of Word Segmentation C. Character segmentation: For segmenting characters, vertical histogram of segmented word is created. Using the histogram, find the points from which the character starts and ends [8]. In a word, spaces between characters are the separators between the characters. ગ, ણ, શ, લ are the major cause for over segmentation resulting into inaccuracy. It is thus necessary to combine the over segmented characters at letter stage to form a single character for recognition as shown in Fig-4. Figure 4. Example of over segmented character 462 C. Feature Extraction In feature extraction stage for every segmented character; feature vector is calculated. Feature vector is a least amount of elements which helps to increase identity rate of character. It also uniquely defines the character. There are mainly three types of feature extraction methods [9]: 1. Statistical 2. Structural 3. Global transformations and moments The major statistical features used for character representation are zoning, projection and profile, crossings and distances. The goal of zoning is to obtain the local characteristics instead of global characteristics. Projection histograms count the number of pixels in each column and row of a character image. The profiles describe well the external shapes of characters and allow distinguishing between a great numbers of letters. Crossings count the number of transitions from background to foreground pixels along vertical and horizontal lines through the character image and Distances calculate the distances of the first image pixel detected from the upper and lower boundaries, of the image, along vertical lines and from the left and right boundaries along horizontal lines [9]. Structural features are based on topological and geometrical properties of the character, such as aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points, horizontal curves at top or bottom, etc [9]. Feature descriptors built from moment functions capture global features, and as such they are well suited for shape and character recognition. Central, Zernike moments that make the process of recognizing an object scale, translation, and rotation invariant. The original image can be completely reconstructed from the moment coefficients [9]. D. Classification and Recognition The classification stage is the decision making part of a recognition system and it uses the

5 features extracted in the previous stage. It is this part of the OCR which finally recognizes individual characters and outputs them in machine editable form. A number of approaches are possible for the design of classifier; and the choice often depends on which classifier is available. A number of classification methods were purposed by different researchers some of these are statistical methods, syntactic methods, template matching, artificial neural networks. In reported work [10], classifiers selected are the k Nearest Neighbor and the Euclidean Minimum Distance Classifier using Regular moments in 8 dimensions and Invariant moments in 6 dimensions and in the 600- dimensional binary space. A recognition rate of 67% is achieved. General Regression Neural Network was used in reported work [11] for the first time as being used by the classifier. The use of GRNN as a classifier for OCR is very good since it saves a lot of time for training. In reported work [12], they used a two-layer feedforward network, with sigmoid hidden and output neurons, that can classify vectors arbitrarily well. Average recognition rate of about % is obtained. In reported paper [13] they have proposed multilayer feed forward neural network with Back propagation learning for the handwritten Gujarati digit recognition for various image sizes. E. Post-processing Post processing for character recognition corrects errors that occurred in the character recognition unit. The outputs of the character recognition unit have several character candidates. Post processing determines the best combination of characters from those character candidates. So in post processing there are mainly two functions: Grouping: The process which performing association of symbols into strings is commonly known to as grouping. The grouping of the symbols into strings is based on the symbols location in the document. Error identification and correction: After groping of Characters we have to identify errors and correct them. One of the approaches is the use of dictionaries, which has proven to be the most efficient method for error identification and correction. Given a word, in which an error may be present, the word is looked up in the dictionary. If the word is not present in the dictionary, an error has been initialized, and may be corrected by changing the word into the most similar word. ACKNOWLEDGMENT I would like to thank my guide Prof. Ashwin Makwana who have guided and supported me throughout the survey work. I owe my deepest gratitude to Prof. Amit Ganatra(HOD), for his guidance, encouragement. I am grateful thank my institute, Chandubhai S. Patel Institute of Technology, for giving me this opportunity to work in the great environment. I would also like to thank my parents, my friends and well wishers who helped me to come out of woods whenever any difficulty was encountered. It was only due to their support, motivation and encouragement that I could steer through the project on an honest course to the splendor of success. REFERENCES [1] Pavlos Stathi, Ergina Kavallieratou, Nikos Papamarkos, An Evaluation Technique for Binarization Algorithms, Journal of Universal Computer Science, vol. 14, no. 18 (2008), [2] Venu Govindaraju,Srirangaraj Setlur, Guide to OCR for Indic Scripts, book published by springer, (pg no:73-96) [3] Yasser Alginahi, Preprocessing Techniques in Character Recognition [4] Deepak Kumar, Dalwinder Singh, A Review of Scanned Document Skew Detection 463

6 and Correction Techniques, IJCST Vol. 3, Issue 2, April - June 2012 [5] Naazia Makkar,Sukhjit Singh, A Brief tour to various Skew Detection and Correction Techniques, International Journal for Science and Emerging Technologies with Latest Trends 4(1): (2012) [6] Mandar Chaudhary, Gitam Shikkenawis, Suman K. Mitra, Mukesh Goswami, Similar looking Gujarati printed character recognition using Locality Preserving Projection and Artificial Neural Networks, Third International Conference on Emerging Applications of Information Technology (EAIT),2012 [7] K.V.Kale, S.V.Chavan, M.M.Kazi, Y.S.Rode, Handwritten Devanagari Compound Character Recognition using Legendre Moment an Artificial Neural Network Approach, International Symposium on Computational and Business Intelligence,2013 [8] Vijay Kumar, Pankaj K. Sengar, Segmentation of Printed Text in Devanagari Script and Gurmukhi Script, International Journal of Computer Applications ( ) Volume 3 No.8, June 2010 [9] Laurence Likforman-Sulem, Abderrazak Zahour, Bruno Taconet, Text Line Segmentation of Historical Documents: a Survey, International Journal on Document Analysis and Recognition, Springer, 2006 [10] Sameer Antani and Lalitha Agnihotri, Gujarati Character Recognition,Document Analysis and Recognition, ICDAR '99. Proceedings of the Fifth International Conference on [11] Jignesh Dholakia, S. Rama Mohan And Atul Negi, Zone Identification in the Printed Gujarati Text, Document Analysis and Recognition, pp Vol. 1 [12] Jignesh Dholakia, Archit Yajnik and Atul Negi, Wavelet Feature Based Confusion Character Sets for Gujarati Script, International Conference on Computational Intelligence and Multimedia Applications,2007 [13] Mrs. Chhaya Patel, Mr. Apurva Desai, Segmentation of Text Lines into Words for Gujarati Handwritten Text, Second International Conference on Emerging Trends in Engineering and Technology,