Version 1
: Received: 30 August 2024 / Approved: 30 August 2024 / Online: 3 September 2024 (05:10:05 CEST)
How to cite:
Mutawa, A.; Allaho, M.; Al-Hajeri, M. Machine Learning Approach for Arabic Handwritten Recognition. Preprints2024, 2024082233. https://doi.org/10.20944/preprints202408.2233.v1
Mutawa, A.; Allaho, M.; Al-Hajeri, M. Machine Learning Approach for Arabic Handwritten Recognition. Preprints 2024, 2024082233. https://doi.org/10.20944/preprints202408.2233.v1
Mutawa, A.; Allaho, M.; Al-Hajeri, M. Machine Learning Approach for Arabic Handwritten Recognition. Preprints2024, 2024082233. https://doi.org/10.20944/preprints202408.2233.v1
APA Style
Mutawa, A., Allaho, M., & Al-Hajeri, M. (2024). Machine Learning Approach for Arabic Handwritten Recognition. Preprints. https://doi.org/10.20944/preprints202408.2233.v1
Chicago/Turabian Style
Mutawa, A., Mohammad Allaho and Monirah Al-Hajeri. 2024 "Machine Learning Approach for Arabic Handwritten Recognition" Preprints. https://doi.org/10.20944/preprints202408.2233.v1
Abstract
Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
