Segmentation vs. non-segmentation based neural techniques for cursive word recognition: an experimental analysis

Abstract

This paper compares segmentation-based and non-segmentation based techniques for cursive 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 fair comparison, 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. Experimental results show that the non-segmentation technique achieves much higher recognition rate than the segmentation based technique.