Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

TL;DR

This paper presents a morphological operator-based model for segmenting offline handwritten Arabic words, achieving 88% accuracy across diverse handwriting styles and outperforming existing methods.

Contribution

The study introduces a novel segmentation framework using morphological operators for gap connection and diacritics removal, improving accuracy on real-world Arabic handwriting data.

Findings

Achieved 88% segmentation accuracy on IESK-ArDB database.

Effectively handled diverse handwriting styles and gaps in offline Arabic words.

Outperformed existing segmentation methods in accuracy.

Abstract

The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.