DEVANAGARI HANDWRITTEN WORD RECOGNITION USING EFFICIENT AND FAST FEED FORWARD NEURAL NETWORK CLASSIFIER.

Handwritten character recognition is attaining popularity due to its potential application areas which would reduce the task of data entry and save the time.Design ofDevnagari handwritten word recognition poses a challenge to the researchers due to the variable size of character, various writing styles & acquisition device used and many other factors. The large character set of 34 consonants and 18 vowels with attached modifiersmakes the Devnagari character recognition very challenging.This paper proposes an effective method for recognition of isolated Marathi handwritten word for Devnagari script. Handwrittenwordrecognition method is composed of three main phases such as Segmentation, Feature extraction and classification. In first phase, input image is preprocessed using Gaussian filter for smoothing and noise removal. Further using thresholding, preprocessed image is segmented with additional morphological operations such as dilation, filling, erosion in order to get finalized segmented image. In second phase, faster and optimized hybrid feature vector of length 91 is presented using combination of geometrical features, regional features, distance transform and gradient features. In third phase, efficient and accurate classifier called Feed Forward Neural Network [FFNN] is presented for online Devnagari handwritten word recognition.This classifier is trained with 91features of training samples. Here 200 commonly used handwritten words are collected from 50 users with different handwriting styles to create database of 10,000 words. For experimentation, 7500 word samples are used to create 15 dataset out of which 70% samples are used for training, 20% for testing & 10% for validation.Overall recognitionaccuracy obtained usingFFNN classifier is 94.57%.