An ensemble of neural networks for nonlinear segmentation of overlapped cursive script

Abstract

Precise character segmentation is the only solution towards higher optical character recognition (OCR) accuracy. In cursive script, overlapped characters are serious issue in the process of character segmentations as characters are deprived from their discriminative parts using conventional linear segmentation strategy. Hence, nonlinear segmentation is an utmost need to avoid loss of characters parts and to enhance character/script recognition accuracy. This paper presents an improved approach for nonlinear segmentation of the overlapped characters in handwritten roman script. The proposed approach presents a set of rules that are derived heuristically to search character boundaries of the cursive script that is validated by using ensemble of neural confidence. Accordingly, correct boundaries are retained and incorrect are removed based on ensemble neural networks vote. Finally, based on verified valid segmentation points, characters are segmented nonlinearly. For fair comparison CEDAR benchmark database is experimented. The experimental results are much better than conventional linear character segmentation techniques reported in the state of art. Ensemble neural network play vital role to enhance character segmentation accuracy as compared to individual neural networks.