SEGMENTATION VERSUS NON-SEGMENTATION BASED NEURAL TECHNIQUES FOR CURSIVE WORD RECOGNITION: AN EXPERIMENTAL ANALYSIS

Abstract

This paper presents a comparative analysis of segmentation and non-segmentation based techniques for cursive handwritten word recognition. In our segmentation based technique, every word is segmented into characters, the chain code features are extracted from segmented characters, the features are fed to neural network classifier and finally the words are constructed using a string compare algorithm. In our non-segmentation based technique, the chain code features are extracted directly from words and the words are fed to a neural network classifier to classify them into word classes. To make a fair comparison, a CEDAR benchmark database is used, and the parameters such as the number of words, thresholding, resizing, feature extraction techniques, etc. are kept same for both the techniques. The experimental results and analysis show that the non-segmentation technique achieves higher recognition rate than the segmentation based technique.