Word Segmentation from Unconstrained Handwritten Bangla Document Images using Distance Transform

TL;DR

This paper presents a novel method for segmenting handwritten Bangla words from unconstrained document images using Distance Transform, achieving high accuracy without over-segmentation issues.

Contribution

It introduces a distance transform-based segmentation approach combined with post-processing to effectively segment handwritten Bangla words from complex images.

Findings

Segmentation accuracy of 91.88% on CMATERdb1.1.1 database

Robustness of the proposed method demonstrated

Effective handling of over- and under-segmentation

Abstract

Segmentation of handwritten document images into text lines and words is one of the most significant and challenging tasks in the development of a complete Optical Character Recognition (OCR) system. This paper addresses the automatic segmentation of text words directly from unconstrained Bangla handwritten document images. The popular Distance transform (DT) algorithm is applied for locating the outer boundary of the word images. This technique is free from generating the over-segmented words. A simple post-processing procedure is applied to isolate the under-segmented word images, if any. The proposed technique is tested on 50 random images taken from CMATERdb1.1.1 database. Satisfactory result is achieved with a segmentation accuracy of 91.88% which confirms the robustness of the proposed methodology.