Region-based touched character segmentation in handwritten words

Abstract

This paper presents an intelligent technique for segmentation of off-line cursive handwritten words particularly on touching characters problem. In this study, Self Organizing Feature Maps (SOM) is implemented to identify the touching portion of the cursive words. The image of the connected characters is preprocessed and the core-zone is detected to overcome ascender and descender of the touched character. Prior to clustering, the pixels of the image were mapped into coordinate system as features vector. These features vector are clustered into three classes: left, right and middle region, and then vertical segmentation is performed using SOM to determine the winner node of middle region. The experiments are conducted using syntactic CCC database. The results show that the proposed algorithm yields promising segmentation output and feasible with other existing techniques.