Abstract: Optical Character recognition (OCR) has enabled many applications as it has attained high accuracy for all printing documents and also for handwriting of many languages. How-ever, the state-of-the-art accuracy of Arabic handwritten word recognition is far behind. Arabic script is cursive (both printed and handwritten). Therefore, traditionally Arabic recognition systems segment a word to characters first before recognizing its characters. Arabic word segmentation is very difficult because Arabic letters contain many dots. Moreover, Arabic letters are context sensitive and some letters overlapped vertically. A holis-tic recognizer that recognizes common words directly (without segmentation) seems the plausible model for recognizing Arabic common words. This paper presents the result of training a Conventional Neural Network (CNN), holistically, to recognize Arabic names. Experiments result shows that the proposed CNN is distinct and significantly superior to other recognizers that were used with the same dataset.

Mohamed Elhafiz Mustafa and Murtada Khalafallah Elbashir, “A Deep Learning Approach for Handwritten Arabic Names Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110183

@article{Mustafa2020,
title = {A Deep Learning Approach for Handwritten Arabic Names Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110183},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110183},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Mohamed Elhafiz Mustafa and Murtada Khalafallah Elbashir}
}