Abstract With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al- Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score.

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[24] Zhou W. and Jia J., “A Learning Framework for Shape Retrieval Based on Multilayer Perceptrons,” Pattern Recognition Letters, vol. 117, pp. 119-130, 2018. Manal Khayyat received the B.Sc. degree (Hons.) in Computer Science from King Abdulaziz University, Saudi Arabia, in 2007 and received M.Sc. degree of Applied Science in Quality Systems Engineering from Concordia University, Canada, in 2015. She is currently a PhD student in the Department of Computer Science at King Abdulaziz University. She worked at the IT department of Effat University, Saudi Arabia, from 2007 to 2010. Then, she worked as a lecturer at King Abdulaziz University, from 2012 to 2019 and she is currently working as a lecturer at Umm Al-Qura University, Saudi Arabia. Her research interests include computer vision, image processing, natural language recognition, and deep learning. Lamiaa Elrefaei received the B.Sc. degree (Hons.) in electrical engineering (electronics and telecommunications), and the M.Sc. and Ph.D. degrees in electrical engineering (electronics) from the Faculty of Engineering at Shoubra, Benha University, Egypt, in 1997, 2003, and 2008, respectively. She held a number of faculty positions at Benha University, as a Teaching Assistant, from 1998 to 2003, as an Assistant Lecturer, from 2003 to 2008, and has been a Lecturer, since 2008. She is currently an Associate Professor with the Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Her research interests include computational intelligence, biometrics, multimedia security, wireless networks, and nano networks.