OPTICAL CHARACTER RECOGNITION FOR SCRIPTS AND DOCUMENTS

Transcription

1 Volume 120 No , ISSN: (on-line version) url: OPTICAL CHARACTER RECOGNITION FOR SCRIPTS AND DOCUMENTS P. Ramani 1,R.Keshav 2, Vishal.D 3,Vigneshwaran.S 4, Isaac Mathew 5, 1 (PhD), 1,2,3,4,5 Department of ECE, SRM Institute of Science and Technology, Chennai 1 ramani.p@srmuniv.ac.in, 2 rkeshav2810@gmail.com 3 visddvs307@gmail.com 4 svigneshwaran230@gmail.com 5 isaacmathew96@gmail.com July 23, 2018 Abstract The Optical character recognition (OCR) system automatically translates or converts scanned images of handwritten, typed or printed text into machine-encoded text with respect to the structure of the scanned image text. This OCR system can be used for recognizing multilingual images by using structural algorithm and shape analysis. This paper is organized into six major sections covering introduction, architecture of multilingual OCR, existing system, proposed model, future prospects and the conclusion. The proposed OCR system handles the existing time and space complexities for achieving higher recognition accuracy and adds new functionalities. Key Words:Digital image processing, OCR, Multilingual, Tamil, Hindi and English scripts, Structure analysis

2 1 Introduction Optical character recognition (OCR) is a system that recognizes and converts image content to printed text module. It has received considerable attention in recent years due to the process of computer recognition of optically scanned and digitized character images that are converted into an electronic text document. The input to the system is achieved by scanning given data. Figure 1. Sample Multilingual Document The output of the system will be in the form of an editable text document that can be stored as a secured file. The character recognition model plays a vital role in the world of text to speech conversion, as it identifies and understands the text in a document. It can be later read out by a computer. We propose a model that recognizes English and other languages of the Indian scripts such as Tamil, English, Telugu, Hindi, Kannada, Marathi and Sanskrit. Some of the practical uses of the OCR are (i)processing cheques without human interference, (ii) reading aid for visually impaired, (iii) automatically entering text into the computer for publishing, library catalogue management and ledgering, (iv) automatically reading city names and addresses for postal mail, (v) natural language processing and (vi) reading inscriptions from heritage sites. The majority of the task depends upon the structural form of the input. The separation of languages depends on the curves and lines involved. The task of separating lines and words in the document is independent of the script and it achieved with conventional projection profile techniques. The system is also based on the concept of Support Vector Machines. The steps of structural algorithm include segmentations such as line and word segmentation. The accuracy of a particular language can also be determined

3 2 Architecture of Multilingual OCR System: The input provided by the user is a scanned image of hand written text or image uploaded from the database. Each part of the system can be controlled by the user by changing the parameters or image effects. To make the software recognize the image, it undergoes some pre-processing steps. They are Image pre-processing Filtering noise RGB to Gray conversion Image orientation Segmentation Image Separation Input Image Processing The desired document to be analyzed is converted into an image format and is given as input in MATLAB. Using specific sub functions, the image is converted from RGB to grey in order eliminate any kind of noise present in the image and enhance its clarity. Line Segmentation For the given input image, the borders are created (upper line, lower line, headline, base line). Then, the image is segregated into two images in which the desired line will appear in one image and rest of the lines in other image. The rows in which there are minimum or absolutely no pixels are eliminated. Word Segmentation Every word in each line is segregated and the respective images are formed irrespective of the language. Figure 2. (a) Sample Input Image for Word Segmentation

4 Figure 2. (b) Outputs for Word Segmentation for the given sample input Separation of Scripts Based on the scripts and patterns (Tick components in Telugu, Upper line in Hindi, Curves in Tamil, Horizontal lines in English etc.), all the words for a single language will be formed as separate images. Thus, different images for different languages are identified and formed. 3 Existing System First generation of Indic OCRs used rule-based solutions and intuitive features for the recognition of characters. Second generation OCRs continued with the definition of characters but moved forward with more principled features based on signal processing or statistical techniques. The existing system includes research work carried out by Peakeand Tan who have proposed a method for automatic script and language identification from document images using multiple channel (Gabour) filters and gray level co-occurrence matrices for seven languages: Chinese, English, Greek, Korean, Malayalam, Persian and Russian. Tan has developed rotation invariant texture feature extraction method for automatic script identification for six languages: Chinese, Greek, English, Russian, Persian and Malayalam. Traditional machine learning based methods used for Indic OCRs used multiclass classification schemes implemented with neural networks or SVMs. However, such solutions demanded two separate modules. Indian language OCRs that were developed until recently used a segmentation scheme prior to recognition. From a machine learning perspective, OCR is more of a structured prediction problem where the output is a sequence of

5 characters/symbols of arbitrary length and input is a sequence of feature vectors of varying length. Our survey for previous research work in this area shows that hardly few attempts have been made to focus on these seven languages Telugu, Tamil, Hindi, English, Sanskrit, Marati and Kannada. Few basic features that are analyzed based on the script patterns are: Top row, Bottom row, Horizontal line, Vertical line, Tick components, Holes, Curves, Dots and Spacing. 4 Proposed Model The proposed concept can be implemented using MATLAB based on structural algorithm or Connected Content approach which specifies the relationship between neighbouring characters or pixels. Tamil language has a set of unique patterns in its script such as curves, dots, etc. The dots occur frequently in the upper line. The letters contain a combination of curves, horizontal and vertical lines. These specific features can be used to distinguish Tamil from other languages. Figure 3. Sample Hindi word after segmentation The top row of the image shows the number of top segments where the intensity of dark pixels is maximum. Pixel value of black is 0 while white is represented as 1.The bottom row of the image shows the number of bottom segments where the intensity of dark pixels is maximum. Pixel value of black is 0 while white is represented as

6 Figure 4. Image Processing Block Diagram When more than 50% of the letters (i.e.) connected components are present below the bottom row, then that connected component is considered as the descendent. Dots are the most important feature in Tamil script as they occur very frequently in the upper row. They occur in Hindi as well but not with a very high frequency. Majority of the Telugu characters have tick mark shaped structures at the top segment of the characters. Also, it could be observed that majority of Telugu characters have upward curves present at their bottom portion. These distinct characteristics of Telugu characters are helpful in separating them from Kannada, Hindi, Tamil, Sanskrit and English languages. It could be noted that Devanagari script contains characters that have a horizontal line at the top part which is called headline that is named as sirorekha in Devanagari as seen in Fig. 3. It joins two or more basic or compound characters to form a word. These head lines are present at the top segment of the characters and they can be used to identify features of Devanagari text. Another strong feature in a Devanagari script is that most of the pixels in the headline and bottom line appear to be similar. This results in both top profile and bottom profile of a Hindi text line to lie at the top part of the characters. However, this distinct feature is absent in both Kannada and English text lines where the density top and bottom profiles occur at different positions. Using these characteristics, Hindi text line can be separated from Kannada, Tamil and English languages. It is found that the distribution of pixels in English characters is regular and symmetric. Due to this uniform pixel distribution in English characters, the density of top and bottom profiles are almost similar. On the contrast, such uniformity is not found in the other five languages Sanskrit, Kannada, Tamil, Telugu and Hindi. Thus, this structural attribute is used in supporting character recognition features in the proposed model

7 5 Conclusion and Future Prospects The proposed approach could successfully identify the seven different language scripts (Telugu, Tamil, English, Sanskrit, Kannada, Marati and Hindi). It is based on the features extracted from the given text words. Currently we have developed shared linked libraries of each module (script-independent or script-dependent module) received by other consortia members. We are testing each language OCR for character level accuracy and word level accuracy. We are in the process of testing for at least thousand pages of each script for character and word level accuracy. It can be powerful enough to discriminate among the prototypes those that most likely will match the sample. Once this subset has been found, a more detailed description is computed, and the main classification step entered. To achieve this purpose, a multilevel description of the character, in terms of the features provided by the feature extractor is given. At the intermediate level, the character is decomposed into components by removing the branch points. The main classifier chooses which one of the prototypes, among the selected ones, has the best matching with the sample. Experiments have proved that the method is correct and efficient. As a future prospect of the OCR project we are trying to tune the recognition to respond well to a variety of fonts and font/point sizes. Also, multilingual OCR could be integrated with Braille interface for all Indian scripts addressed here. We can aim of developing large Document Management Systems 6 Result Implementing the enlisted steps in MATLAB for languages like Tamil, Kannada, English, Telugu, Marati, Sanskrit, Hindi the following result of accuracy was obtained which is shown in the efficiency table given below

8 Table: Efficiency References [1] Gaurav Harit, K. J. Jinesh, Ritu Garg C.V Jawahar and Santanu Chaudhury Managing Multilingual OCR Project using XML Proc. of International Workshop on Multilingual OCR 2009 Barcelona, Spain. [2] Tushar Patnaik, Shalu Gupta and Gaurav K. Rai. Performance evaluation for Indian Languages in Consortia based OCR. AS- CNT 2009, CDAC, Noida. [3] A. Lear, XML seen as integral to application integration, IT Professional, vol.1, no. 5, pp. 1216, Sep/Oct [4] S Rice, J Kanai and T Nartker, An evaluation of OCR accuracy, UNLV Annual Re- port, pp 9-33, [5] J Esakov, D. P. Lopresti and J. S Sandberg, Classification and distribution of Optical Character Recognition errors, SPIE Vol. 2181, Document Recognition,

9 [6] P. B. Pati and A. G. Ramakrishnan, OCR in Indian scripts: A Survey, IETE Technical Re- view, May-Jun 2005, 22(3): [7] K. G. Aparna and A. G. Ramakrishnan, A complete Tamil Optical Character Recognition System, Proc. Fifth IAPR Workshop on Document Analysis Systems DAS-02, Princeton, NJ, August 19-21, 2002, pp [8] B. Vijay Kumar and A. G. Ramakrishnan, Machine Recognition of Printed Kannada Text, Proc. Fifth IAPR Workshop on Document Analysis Systems (DAS-02), August 19-21, 2002, Springer Verlag, Berlin. [9] R S Umesh, Peeta Basa Pati and A G Ramakrishnan, Design of a bilingual Kannada-English OCR, in the book Guide to OCR for Indic Scripts: Document Recognition and Retrieval Springer, 2009 in the Advances in Pattern Recognition Series. Ed: Venu Govindaraju and Setlur Srirangaraj. pp ISBN: [10] Karthika Mohan and C.V.Jawahar A Post-Processing Scheme for Telugu using Statistical Sub-character Language Models Proceedingsof Ninth IAPR International Workshop on Document Analysis Systems (DAS 10), pp , 9-11 June, 2010, Boston, MA, USA. [11] C.V. Jawahar and Anand Kumar Content-level Annotation of Large Collection of Printed Document Images Proc of 9thInternational Conference on Document Analysis and Recognition, Brazil, September, [12] U. Pal, B. B. Chaudhuri: Indian script character recognition: a survey. Pattern Recognition 37(9): (2004) [13] V. Govindaraju and S. Setlur (Editors), Guide to OCR for Indic Scripts, Springer, Sep [14] J.Hochberg, P.Kelly, T.Thomas, L.Kerns, Automatic Script Identification from Document Images using Cluster based Templates, IEEE Transaction on Pattern Analysis and Machine Intelligence, ,1997. Gopal Datt Joshi, Saurabh

10 Garg, Jayanthi Sivaswamy, Script Identification from Indian Documents, DAS 2006, LNCS3872, , [15] Swamy Das M,segmentation of overlapping text lines, characters in printed telugu text document Images International Journal of Engineering Science and Technology Vol. 2(11), 2010, [16] Rajesh Gopakumar, N.V.Subbareddy, Krishnamoorthi Makkaithaya, U.Dinesh Acharya Script Identification from Multilingual Indian Documents using Structural Features, Journal of Computing,Vol.2, Issue 7, July [17] J.Michael Fitzpatrick and John D. Crocetti,Introduction to Programming with MATLAB-Text Book. [18] Alasdair McAndrew, An Introduction to Digital Image Processing with MATLAB-Text Book. [19] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing using MATLAB- Text Book. [20] Haris Papasaika Hanusch, Digital Image Processing using MATLAB-Text Book

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