Cursive Multilingual Characters Recognition Based on Hard Geometric Features
This addresses a persistent accuracy gap in multilingual character recognition for specific languages, though it appears incremental in method.
The paper tackles the problem of low accuracy in cursive multilingual character recognition for Arabic, Persian, and Urdu languages by proposing an automated approach based on geometric features and a BPN to correct segmentation errors, achieving rapid improvement in character recognition accuracy.
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.