An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script

TL;DR

This paper introduces a non-linear segmentation method for overlapped cursive characters using heuristic rules and ensemble neural networks, significantly improving OCR accuracy on handwritten roman script.

Contribution

It presents a novel non-linear segmentation approach combining heuristic geometrical rules with ensemble neural network validation, outperforming traditional linear methods.

Findings

Enhanced segmentation accuracy on CEDAR benchmark

Ensemble neural networks outperform individual models

Significant improvement over conventional linear segmentation techniques

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, non-linear 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 non-linear segmentation of the overlapped characters in handwritten roman script. The proposed technique is composed of a sequence of heuristic rules based on geometrical features of characters to locate possible non-linear character boundaries in a cursive script word. However, to enhance efficiency, heuristic approach is integrated with trained ensemble neural network validation strategy for verification of character boundaries. 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 non-linearly. 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.