Word Segmentation from Unconstrained Handwritten Bangla Document Images using Distance Transform
This work addresses a key challenge in OCR systems for Bangla handwriting, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of segmenting words from unconstrained handwritten Bangla document images using a Distance Transform algorithm, achieving a segmentation accuracy of 91.88% on a test set of 50 images.
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.