Soft computing techniques for optical character recognition systems

Abstract

The continuous increase in demand to discover robust and low cost optical character recognition (OCR) systems has prompted researchers to look for rigorous methods of character recognition. In the past OCR systems have been built through traditional pattern recognition and machine learning approaches. There has always been a quest to develop best OCR products which satisfy the user’s needs. Since past few decades soft computing techniques have come up as a promising candidate for the development of cost effective OCR systems. Some important soft computing techniques for optical character recognition (OCR) systems are presented in this chapter. They are hough transform for fuzzy feature extraction, genetic algorithms (GA) for feature selection, fuzzy multilayer perceptron (FMLP), rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM), fuzzy rough versions of support vector machine (FRSVM), hierarchical fuzzy bidirectional recurrent neural networks (HFBRNN) and fuzzy markov random fields (FMRF). These techniques are used for developing OCR systems for different languages viz English, French, German, Latin, Hindi and Gujrati languages. The soft computing methods are used in the different steps of OCR systems discussed in Chap. 2. A comprehensive assessment of these methods is performed in Chaps. 4–9 for the stated languages. A thorough understanding of this chapter will help the readers to appreciate the reading material presented in the abovementioned chapters.