Open AccessArabic digit recognition using robust deep convolution neural network
Author(s) -
Qasim Mahdi Haref,
Mohsin N. Srayyih Al-Maliki,
Maytham Mohammed Tuaama,
Hayder M. Albehadili
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1530/1/012085
Subject(s) - robustness (evolution) , digit recognition , convolution (computer science) , computer science , numerical digit , convolutional neural network , arabic , artificial intelligence , arabic numerals , deep neural networks , pattern recognition (psychology) , deep learning , speech recognition , artificial neural network , detector , arithmetic , mathematics , telecommunications , biochemistry , chemistry , linguistics , philosophy , gene
Recently, digit recognition becomes one of interest problems for many researchers. However, Arabic digits have lack for such research. In this work, we used a robust deep convolution neural network (DCNN) to evaluate our collected Arabic digit dataset. We introduce substantial changes to CNN models to achieve superior results to prior work. Extensive experimental results are contacted to show the robustness of proposed models. Our detector achieves best current state-of-the-art results.