The Optimal Model for Arabic Optical Character Recognition(OCR) Text A Comprehensive Study

Abstract

Optical Character Recognition (OCR) technology has evolved significantly over the past few decades, enabling the conversion of printed or handwritten text into machine-encoded text. While OCR systems for languages like English have reached a high level of accuracy, Arabic OCR remains a challenging task due to the language's unique characteristics, such as its cursive nature, diacritics, and complex script. This research aims to explore and evaluate the best models for Arabic OCR text, focusing on recent advancements in deep learning and machine learning techniques. The study will review existing models, assess their performance, and provide recommendations for future research and development in this field.

This research provides a comprehensive overview of the best models for Arabic OCR text, highlighting the strengths and limitations of various approaches. The findings suggest that hybrid models, particularly those that combine CNNs and LSTMs, are the most effective for handling the complexities of Arabic script. Future research should focus on data augmentation, transfer learning, attention mechanisms, and multi-task learning to further improve the accuracy of Arabic OCR systems.