Cursive script segmentation with neural confidence

Abstract

This paper presents a new, simple and fast approach for character segmentation of unconstrained handwritten words. The proposed approach first seeks the possible character boundaries based on characters geometric features analysis. However, due to inherited ambiguity and a lack of context, few characters are over-segmented. To increase the efficiency of the proposed approach, an Artificial Neural Network is trained with significant number of valid segmentation points for cursive handwritten words. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison, benchmark database CEDAR is used. The experimental results are promising from complexity and accuracy points of view.