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Abstract-Character Recognition is one of the important tasks in Pattern Recognition. The complexity of the character recognition problem depends on the character set to be recognized. Neural Network is one of the most widely used and popular techniques for character recognition problem. This paper discusses the classification and recognition of printed Hindi Vowels and Consonants using Artificial Neural Networks. The vowels and consonants in Hindi characters can be divided in to sub groups based on certain significant characteristics. For each group, a separate network is designed and trained to recognize the characters which belong to that group. When a test character is given, appropriate neural network is invoked to recognize the character in that group, based on the features in that character. The accuracy of the network is analyzed by giving various test patterns to the system.

Optical character recognition is a vital task in the field of pattern recognition. English character recognition has been extensively studied by many researchers but in case of Indian languages which are complicated; the research work is very limited. Devanagari is an indian script used by huge number of indian people. Devanagari forms the basis for several indian languages including Hindi, Sanskrit, Kashmiri, Marathi and so on. This article presents a review of earlier research work related to devanagari character recognition along with some applications of optical character recognition system.

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