Abstract: English character has been extensively studied in last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. The problem arises in Devanagari character recognition provides less correctness and efficiency so we are using neural network and genetic algorithm to overcome that problem. Devanagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. Devnagari script include Marathi, Nepali , hindi and Sanskrit languagesí. This report is intended to serve as a guide and update for the readers, working in the Devanagari Character Recognition area, it is now widely accepted that a single classification algorithm canít yields better performance rate, so we are using here not only neural network but also genetic algorithm. Though, various techniques are well experimented by many researchers, an attempt is made to enhance the existing results by using features like glcm ,histogram, color domino.

Keywords: Character recognition, segmentation, feature extraction, genetic algorithm.