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In the fast moving world with the amazingly growing technology, character recognitions play a wide role by providing more scope to perform research in OCR techniques. Sanskrit handwritten recognition has been one of the challenging research areas in the field of pattern recognition. Character recognition is the electronic translation of scanned images of handwritten or printed text into a machine encoded text. The character recognition is a standout amongst the most generally utilized biometric attributes for authentication of persons and document. In this paper proposed an off line handwritten character recognition framework utilizing feed forward neural network. A handwritten Sanskrit character is resized into 20x30 pixels and this character is used for training the neural network. After the training process, the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Sanskrit characters has been calculated and compared. The results of the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems. Keywords: Classification, Feed Forward Neural Network, Handwritten Sanskrit Character Recognition, Image Extraction, Pre-Processing
Dineshkumar et al. (Fri,) studied this question.