Spectral Analysis of Projection Histogram for Enhancing Close matching character Recognition in Malayalam

TL;DR

This paper proposes a spectral analysis-based classifier using support vector machines to improve Malayalam OCR accuracy by better distinguishing close-matching characters, addressing a key bottleneck in recognition systems.

Contribution

It introduces a novel spectral coefficient feature extraction method and a specialized classifier for close-matching Malayalam characters to enhance OCR accuracy.

Findings

Improved recognition accuracy for close-matching characters.

Spectral coefficient features effectively distinguish similar characters.

Enhanced overall OCR performance in Malayalam documents.

Abstract

The success rates of Optical Character Recognition (OCR) systems for printed Malayalam documents is quite impressive with the state of the art accuracy levels in the range of 85-95% for various. However for real applications, further enhancement of this accuracy levels are required. One of the bottle necks in further enhancement of the accuracy is identified as close-matching characters. In this paper, we delineate the close matching characters in Malayalam and report the development of a specialised classifier for these close-matching characters. The output of a state of the art of OCR is taken and characters falling into the close-matching character set is further fed into this specialised classifier for enhancing the accuracy. The classifier is based on support vector machine algorithm and uses feature vectors derived out of spectral coefficients of projection histogram signals of close-matching characters.