Performance analysis of segmentation approach for cursive handwriting on benchmark database

Abstract

The purpose of this paper is to analyze improved performance of our segmentation algorithm on IAM benchmark database in comparison to others available in the literature from accuracy and complexity points of view. Segmentation is achieved by analyzing ligatures which are strong points for segmentation of cursive handwritten words. Following preprocessing, a new heuristic technique is employed to over-segment each word at potential segmentation points. Subsequently, a simple criterion is performed to come out with fine segmentation points based on character shape analysis. Finally, the fine segmentation points are fed to train neural network for validating segment points to enhance accuracy. Based on detailed analysis and comparison, it was observed that proposed approach increased the segmentation accuracy with minimum computational complexity.