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Script Identification of Text Words from a Tri-Lingual Document Using Voting Technique
M C Padma, P. A. Vijaya
Pages - 35 - 52     |    Revised - 25-02-2010     |    Published - 31-03-2010
Volume - 4   Issue - 1    |    Publication Date - March 2010  Table of Contents
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KEYWORDS
Multi-lingual document processing, Script Identification, Feature Extraction, Binary Tree Classifier
ABSTRACT
In a multi script environment, majority of the documents may contain text information printed in more than one script/language forms. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. In this context, this paper proposes to develop a model to identify and separate text words of Kannada, Hindi and English scripts from a printed tri-lingual document. The proposed method is trained to learn thoroughly the distinct features of each script. The binary tree classifier is used to classify the input text image. Experimentation conducted involved 1500 text words for learning and 1200 text words for testing. Extensive experimentation has been carried out on both manually created data set and scanned data set. The results are very encouraging and prove the efficacy of the proposed model. The average success rate is found to be 99% for manually created data set and 98.5% for data set constructed from scanned document images.
CITED BY (4)  
1 Singh, P. K., Sarkar, R., & Nasipuri, M. (2015). Offline Script Identification from multilingual Indic-script documents: A state-of-the-art. Computer Science Review, 15, 1-28.
2 Santhosh, R., & Srinivasa, G. (2012, August). A compact feature set for recognition of handwritten numerals and vowels in the Kanarese script. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 805-811). ACM.
3 Badhika, S. (2012). Multilevel Segmentation for OCR.
4 Badhika, S. An Empirical Study on Identification of Strokes and their Significance in Script Identification.
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1 U.Pal, B.B.Choudhuri, “Script Line Separation From Indian Multi-Script Documents”, 5th Int. Conference on Document Analysis and Recognition(IEEE Comput. Soc. Press), 406-409, (1999).
2 T.N.Tan, “Rotation Invariant Texture Features and their use in Automatic Script Identification”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 751- 756, (1998).
3 U. Pal, S. Sinha and B. B. Chaudhuri, “Multi-Script Line identification from Indian Documents”, In Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) 0-7695-1960-1/03 © 2003 IEEE, vol.2, pp.880-884, (2003).
4 Santanu Choudhury, Gaurav Harit, Shekar Madnani, R.B. Shet, “Identification of Scripts of Indian Languages by Combining Trainable Classifiers”, ICVGIP, Dec.20-22, Bangalore, India, (2000).
5 S. Chaudhury, R. Sheth, “Trainable script identification strategies for Indian languages”, In Proc. 5th Int. Conf. on Document Analysis and Recognition (IEEE Comput. Soc. Press), pp. 657–660, 1999.
6 Gopal Datt Joshi, Saurabh Garg and Jayanthi Sivaswamy, “Script Identification from Indian Documents”, LNCS 3872, pp. 255-267, DAS (2006).
7 S.Basavaraj Patil and N V Subbareddy, “Neural network based system for script identification in Indian documents”, Sadhana Vol. 27, Part 1, pp. 83–97. © Printed in India, (2002).
8 B.V. Dhandra, Mallikarjun Hangarge, Ravindra Hegadi and V.S. Malemath, “Word Level Script Identification in Bilingual Documents through Discriminating Features”, IEEE - ICSCN 2007, MIT Campus, Anna University, Chennai, India. pp.630-635. (2007).
9 Lijun Zhou, Yue Lu and Chew Lim Tan, “Bangla/English Script Identification Based on Analysis of Connected Component Profiles”, in proc. 7th DAS, pp. 243-254, (2006).
10 G. S. Peake and T. N. Tan, “Script and Language Identification from Document Images”, Proc. Workshop Document Image Analysis, vol. 1, pp. 10-17, 1997.
11 M. C. Padma and P.Nagabhushan, “Identification and separation of text words of Karnataka, Hindi and English languages through discriminating features”, in proc. of Second National Conference on Document Analysis and Recognition, Karnataka, India, pp. 252-260, (2003).
12 Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, “Digital Image Processing using MATLAB”, Pearson Education, (2004).
13 Vipin Gupta, G.N. Rathna, K.R. Ramakrishnan, “A Novel Approach to Automatic Identification of Kannada, English and Hindi Words from a Trilingual Document”, Int. conf. on Signal and Image Processing, Hubli, pp. 561-566, (2006).
14 S. L. Wood, X. Yao, K. Krishnamurthy and L. Dang, “Language identification for printed text independent of segmentation”, Proc. Int. Conf. on Image Processing, pp. 428–431, 0-8186- 7310-9/95, 1995 IEEE.
15 J. Hochberg, L. Kerns, P. Kelly and T. Thomas, “Automatic script identification from images using cluster based templates”, IEEE Trans. Pattern Anal. Machine Intell. Vol. 19, No. 2, pp. 176–181, 1997.
16 Shivakumar, Nagabhushan, Hemanthkumar, Manjunath, 2006, “Skew Estimation by Improved Boundary Growing for Text Documents in South Indian Languages”, VIVEKInternational Journal of Artificial Intelligence, Vol. 16, No. 2, pp 15-21.
17 Andrew Busch, Wageeh W. Boles and Sridha Sridharan, “Texture for Script Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 11, pp. 1720- 1732, Nov. 2005.
18 A. L. Spitz, “Determination of script and language content of document images”, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 19, No.3, pp. 235–245, 1997.
19 Ramachandra Manthalkar and P.K. Biswas, “An Automatic Script Identification Scheme for Indian Languages”, IEEE Tran. on Pattern Analysis And Machine Intelligence, vol.19, no.2, pp.160-164, Feb.1997.
20 Hiremath P S and S Shivashankar, “Wavelet Based Co-occurrence Histogram Features for Texture Classification with an Application to Script Identification in a Document Image”, Pattern Recognition Letters 29, 2008, pp 1182-1189.
Mr. M C Padma
Dept. of Computer Science and Engineering, PES College of Engineering, Mandya India - India
padmapes@gmail.com
Associate Professor P. A. Vijaya
- India