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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Classification and Learning Methods for Character Recognition: Advances and Remaining Problems
verfasst von : Cheng-Lin Liu, Hiromichi Fujisawa
Erschienen in: Machine Learning in Document Analysis and Recognition
Verlag: Springer Berlin Heidelberg
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Pattern classification methods based on learning-from-examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This kind of methods include statistical methods, artificial neural networks, support vector machines, multiple classifier combination, etc. In this chapter, we briefly review the learning-based classification methods that have been successfully applied to character recognition, with a special section devoted to the classification of large category set. We then discuss the characteristics of these methods, and discuss the remaining problems in character recognition that can be potentially solved by machine learning methods.