A Deep Learning Based Prediction of Arabic
Manuscripts Handwriting Style
Manal Khayyat1 and Lamiaa Elrefaei2
1Computer Science Department, King Abdulaziz
University, Saudi Arabia
2Electrical
Engineering Department, Benha University, Egypt
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.
Keywords: Deep Learning Model, Convolutional
Neural Network, Handwriting Style Prediction, Arabic Manuscript Images.
Received
October 6, 2019; accepted April 6, 2020
https://doi.org/10.34028/iajit/17/5/3