Integrating CNN and transformer architectures for superior Arabic printed and handwriting characters classification

Integrating CNN and transformer architectures for superior Arabic printed and handwriting characters classification

Abstract Optical Character Recognition (OCR) systems play a crucial role in converting printed Arabic text into digital formats, enabling various applications such as education and digital archiving. However, the complex characteristics of the Arabic script, including its cursive nature, diacritical...

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Bibliographic Details
Main Authors: Mohammed R. Al-Maamari, Rakesh Ramteke, Aymen M. Al-Hejri, Sultan S. Alshamrani
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Arabic OCR
Arabic printed and handwriting
Transfer learning
Ensemble learning
Vision transformer encoder
Self-attention
Online Access:https://doi.org/10.1038/s41598-025-12045-z
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https://doi.org/10.1038/s41598-025-12045-z

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