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
相关论文
共 50 条
  • [41] An Active Shape Model based approach for Arabic Handwritten Character Recognition
    Dinges, Laslo
    Al-Hamadi, Ayoub
    Elzobi, Moftah
    PROCEEDINGS OF 2012 IEEE 11TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) VOLS 1-3, 2012, : 1194 - 1197
  • [42] Databases for recognition of handwritten Arabic cheques
    Al-Ohali, Y
    Cheriet, M
    Suen, C
    PATTERN RECOGNITION, 2003, 36 (01) : 111 - 121
  • [43] A Survey on Arabic Handwritten Character Recognition
    Ali A.A.A.
    Suresha M.
    Ahmed H.A.M.
    SN Computer Science, 2020, 1 (3)
  • [44] Advancing Accurate Recognition of Handwritten Arabic Character: An Innovative Hybrid Approach∗
    Khoudour, Mohamed Elamine
    Biskri, Ismail
    Layadi, Fouad Abdallah
    6th Edition of the International Conference on Advanced Aspects of Software Engineering, ICAASE 2024 - Proceedings, 2024,
  • [45] A transformer-based approach for Arabic offline handwritten text recognition
    Momeni, Saleh
    Babaali, Bagher
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (04) : 3053 - 3062
  • [46] Recognition of Isolated Handwritten Arabic Characters
    Almansari, Osamah Abdulrahman
    Hashim, Nik Nur Wahidah Nik
    2019 7TH INTERNATIONAL CONFERENCE ON MECHATRONICS ENGINEERING (ICOM), 2019, : 107 - 111
  • [47] A Deep Learning Approach to Handwritten Number Recognition
    Ruiz, Victoria
    Gonzalez de Lena, Maria T.
    Sueiras, Jorge
    Sanchez, Angel
    Velez, Jose F.
    BIOMEDICAL APPLICATIONS BASED ON NATURAL AND ARTIFICIAL COMPUTING, PT II, 2017, 10338 : 193 - 202
  • [48] A Database for Arabic Handwritten Character Recognition
    AlKhateeb, Jawad H.
    INTERNATIONAL CONFERENCE ON COMMUNICATIONS, MANAGEMENT, AND INFORMATION TECHNOLOGY (ICCMIT'2015), 2015, 65 : 556 - 561
  • [49] Arabic handwritten document preprocessing and recognition
    Chammas, Edgard
    Mokbel, Chafic
    Likforman-Sulem, Laurence
    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), 2015, : 451 - 455
  • [50] The QCRI Recognition System for Handwritten Arabic
    Stahlberg, Felix
    Vogel, Stephan
    IMAGE ANALYSIS AND PROCESSING - ICIAP 2015, PT II, 2015, 9280 : 276 - 286