Open AccessArabic Handwriting Classification using Deep Transfer Learning Techniques
Author(s) -
Ali Abd Almisreb,
Nooritawati Md Tahir,
Sherzod Turaev,
Mohammed A. Saleh,
Syed Abdul Mutalib Al Junid
Publication year - 2022
Publication title -
pertanika journal of science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.174
H-Index - 15
eISSN - 2231-8526
pISSN - 0128-7680
DOI - 10.47836/pjst.30.1.35
Subject(s) - handwriting , computer science , artificial intelligence , transfer of learning , arabic , deep learning , natural language processing , speech recognition , pattern recognition (psychology) , linguistics , philosophy
Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.