Machine Learning Approach for Arabic Handwritten Recognition

被引:0
|
作者
Mutawa, A. M. [1 ,2 ]
Allaho, Mohammad Y. [1 ]
Al-Hajeri, Monirah [1 ]
机构
[1] Kuwait Univ, Coll Engn & Petr, Dept Comp Engn, Safat 13060, Kuwait
[2] Univ Hamburg, Comp Sci Dept, D-22527 Hamburg, Germany
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 19期
关键词
machine learning; handwritten recognition systems; Arabic handwriting; BiLSTM; ResNet; natural language processing; NEURAL-NETWORK;
D O I
10.3390/app14199020
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 conducted 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. In the new model, 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 a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.
引用
收藏
页数:18
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