Abstract
The optical character recognition (OCR) systems for English language were the most primitive ones and occupy a significant place in pattern recognition. The English language OCR systems have been used successfully in a wide array of commercial applications. The different challenges involved in the OCR systems for English language is investigated in this chapter. The pre-processing activities such as binarization, noise removal, skew detection and correction, character segmentation and thinning are performed on the datasets considered. The feature extraction is performed through discrete cosine transformation. The feature based classification is performed through important soft computing techniques viz fuzzy multilayer perceptron (FMLP), rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM) and fuzzy rough support vector machine (FRSVM). 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.
Cheriet, M., Kharma, N., Liu, C. L., Suen, C. Y., Character Recognition Systems: A Guide for Students and Practitioners, John Wiley and Sons, 2007.
http://www.iam.unibe.ch/fki/databases/iam-handwriting-database.
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., Applications of Support Vector Machines in Engineering and Science, Technical Report, Birla Institute of Technology Mesra, Patna Campus, India, 2011.
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.
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.
Jain, A. K., Fundamentals of Digital Image Processing, Prentice Hall, India, 2006.
Russ, J. C., The Image Processing Handbook, CRC Press, 6th Edition, 2011.
Young, T. Y., Fu, K. S., Handbook of Pattern Recognition and Image Processing, Academic Press, 1986.
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., Basak, J., Pal, S. K., Neuro-Fuzzy Feature Evaluation with Theoretical Analysis, Neural Networks, 12(10), pp 1429–1455, 1999.
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.
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Chaudhuri, A., Mandaviya, K., Badelia, P., Ghosh, S.K. (2017). Optical Character Recognition Systems for English 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_4
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DOI: https://doi.org/10.1007/978-3-319-50252-6_4
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