Enhancing Arabic Sign Language Recognition using Deep Learning
Noor S. Sagheer1, Faezah Hamad Almasoudy2, Manar Hamza Bashaa3
1Noor S. Sagheer, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.
2Faezah Hamad Almasoudy, Department of Animals Production, College of Agriculture, Kerbala University, Kerbala, Iraq.
3Manar Hamza Bashaa, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.
Manuscript received on 16 March 2024 | Revised Manuscript received on 15 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 18-23 | Volume-13 Issue-5, April 2024 | Retrieval Number: 100.1/ijitee.E984413050424 | DOI: 10.35940/ijitee.E9844.13050424
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present time, Sign language is essential for people who suffer from hearing loss or who cannot speak. Normal humans tend to overlook the significance of sign language, which is a vital means of communication for deaf and mute individuals. This study proposes a developed model for sign language recognition in Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then, set up the algorithm by developing a program using OpenCV and the Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, each containing 1,500 images that incorporated hand gestures in various environments. The dataset was divided into a training set (70%), a testing set (20%), and a validation set (10%). The results show that the suggested model achieved an accuracy rate of 94.8%, demonstrating its effectiveness and success, particularly after being tried and tested by several users and receiving their comments and feedback.
Keywords: Arabic Language, CNN Classification, Deep Learning, Image Classification.
Scope of the Article: Classification