Handwritten English, Marathi and Sanskrit Word Recognition using Artificial Neural Network and Feature Extraction
Character recognition plays an important role in many recent applications. In recent times among worldwide researchers there is increasing trend to identify handwritten words of many languages and scripts. Various feature extraction techniques are used such as Stroke method, Fourier descriptor, Gradient feature extraction, and chain code histogram. The recognition ratio can be increased by a proper feature extraction technique. Curvelet transform supports curve as well as edge discontinuities. Several group of curveletcoeﬃcient are generated at diﬀerent scale after the curvelet transform and angles. It is found that Neural Network gives enhanced accuracy for recognition purpose.In this an effort is made to recognize handwritten characters for English alphabets also with feature extraction using Multilayer Feed Forward Neural Network. Each character data set contains 26 alphabets and primarily 2 symbols viz. full stop & comma. 50 diverse character data sets are used for training the neural network. In the proposed system, each character is resized into 30x20 pixels, which will directly be used for training. That is each resized character has 600 pixels and these pixels are taken as features for training the neural network.
Keywords - Curvelet transform, Neural Network (MLP), Multilayer Feed forward Network.