Abstract
The optical character recognition (OCR) systems for Latin language were the most primitive ones and occupy a significant place in pattern recognition. The Latin language OCR systems have been used successfully in a wide array of commercial applications. The different challenges involved in the OCR systems for Latin language is investigated in this Chapter. The pre-processing activities such as text region extraction, skew detection and correction, binarization, noise removal, character segmentation and thinning are performed on the datasets considered. The feature extraction is performed through fuzzy Genetic Algorithms (GA). The feature based classification is performed through important soft computing techniques viz rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM) and fuzzy rough support vector machine (FRSVM) and hierarchical fuzzy bidirectional recurrent neural networks (HFBRNN). The superiority of soft computing techniques is demonstrated through the experimental results.
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References
Chaudhuri, A., Some Experiments on Optical Character Recognition Systems for different Languages using Soft Computing Techniques, Technical Report, Birla Institute of Technology Mesra, Patna Campus, India, 2010.
Schantz, H. F., The History of OCR, Recognition Technology Users Association, Manchester Centre, VT, 1982.
Bunke, H., Wang, P. S. P. (Editors), Handbook of Character Recognition and Document Image Analysis, World Scientific, 1997.
Chaudhuri, A., Applications of Support Vector Machines in Engineering and Science, Technical Report, Birla Institute of Technology Mesra, Patna Campus, India, 2011.
Cheriet, M., Kharma, N., Liu, C. L., Suen, C. Y., Character Recognition Systems: A Guide for Students and Practitioners, John Wiley and Sons, 2007.
Chaudhuri, A., De, K., Job Scheduling using Rough Fuzzy Multi-Layer Perception Networks, Journal of Artificial Intelligence: Theory and Applications, 1(1), pp 4–19, 2010.
Chaudhuri, A., De, K., Chatterjee, D., Discovering Stock Price Prediction Rules of Bombay Stock Exchange using Rough Fuzzy Multi-Layer Perception Networks, Book Chapter: Forecasting Financial Markets in India, Rudra P. Pradhan, Indian Institute of Technology Kharagpur, (Editor), Allied Publishers, India, pp 69–96, 2009.
Pal, S. K., Mitra, S., Mitra, P., Rough-Fuzzy Multilayer Perception: Modular Evolution, Rule Generation and Evaluation, IEEE Transactions on Knowledge and Data Engineering, 15(1), pp 14–25, 2003.
Chaudhuri, A., Modified Fuzzy Support Vector Machine for Credit Approval Classification, AI Communications, 27(2), pp 189–211, 2014.
Chaudhuri, A., De, Fuzzy Support Vector Machine for Bankruptcy Prediction, Applied Soft Computing, 11(2), pp 2472–2486, 2011.
Chaudhuri, A., Fuzzy Rough Support Vector Machine for Data Classification, International Journal of Fuzzy System Applications, 5(2), pp 26–53, 2016.
Chaudhuri, A., Ghosh, S. K., Sentiment Analysis of Customer Reviews Using Robust Hierarchical Bidirectional Recurrent Neural Network, Book Chapter: Artificial Intelligence Perspectives in Intelligent Systems, Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova, Petr Silhavy, Zdenka Prokopova, (Editors), Advances in Intelligent Systems and Computing, Springer International Publishing, Switzerland, Volume 464, pp 249–261, 2016.
Taghva, K., Borsack, J., Condit, A., Erva, S., The Effects of Noisy Data on Text Retrieval, Journal of American Society for Information Science, 45 (1), pp 50–58, 1994.
Young, T. Y., Fu, K. S., Handbook of Pattern Recognition and Image Processing, Academic Press, 1986.
Taghva, K., Borsack, J., Condit, A., Effects of OCR Errors on Ranking and Feedback using the Vector Space Model, Information Processing and Management, 32(3), pp 317–327, 1996.
Taghva, K., Borsack, J., Condit, A., Evaluation of Model Based Retrieval Effectiveness with OCR Text, ACM Transactions on Information Systems, 14(1), pp 64–93, 1996.
Jain, A. K., Fundamentals of Digital Image Processing, Prentice Hall, India, 2006.
Russ, J. C., The Image Processing Handbook, CRC Press, 6th Edition, 2011.
Gonzalez, R. C., Woods, R. E., Digital Image Processing, 3rd Edition, Pearson, 2013.
Jain, A. K., Duin, R. P. W., Mao, J., Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), pp 4–37, 2000.
De, R. K., Pal, N. R., Pal, S. K., Feature Analysis: Neural Network and Fuzzy Set Theoretic Approaches, Pattern Recognition, 30(10), pp 1579–1590, 1997.
De, R. K., Basak, J., Pal, S. K., Neuro-Fuzzy Feature Evaluation with Theoretical Analysis, Neural Networks, 12(10), pp 1429–1455, 1999.
Zadeh, L. A., Fuzzy Sets, Information and Control, 8(3), pp 338–353, 1965.
Zimmermann, H. J., Fuzzy Set Theory and its Applications, 4th Edition, Kluwer Academic Publishers, Boston, 2001.
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Chaudhuri, A., Mandaviya, K., Badelia, P., Ghosh, S.K. (2017). Optical Character Recognition Systems for Latin Language. In: Optical Character Recognition Systems for Different Languages with Soft Computing. Studies in Fuzziness and Soft Computing, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-319-50252-6_7
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DOI: https://doi.org/10.1007/978-3-319-50252-6_7
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