Cursive Multilingual Characters Recognition Based on Hard Geometric Features

TL;DR

This paper introduces a geometric feature-based method for recognizing cursive multilingual characters, specifically Arabic, Persian, and Urdu, using a backpropagation network to improve segmentation and classification accuracy.

Contribution

It proposes a novel approach combining geometric features and a specialized BPN to enhance recognition of cursive multilingual scripts without relying on dictionaries.

Findings

Effective segmentation of cursive multilingual characters.

Improved recognition accuracy with the proposed BPN.

Utilization of benchmark dataset for evaluation.

Abstract

The cursive nature of multilingual characters segmentation and recognition of Arabic, Persian, Urdu languages have attracted researchers from academia and industry. However, despite several decades of research, still multilingual characters classification accuracy is not up to the mark. This paper presents an automated approach for multilingual characters segmentation and recognition. The proposed methodology explores character based on their geometric features. However, due to uncertainty and without dictionary support few characters are over-divided. To expand the productivity of the proposed methodology a BPN is prepared with countless division focuses for cursive multilingual characters. Prepared BPN separates off base portioned indicates effectively with rapid upgrade character acknowledgment precision. For reasonable examination, only benchmark dataset is utilized.