3d8d135818d1675848a19e6 20240423081623917 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 04 30 2024 13 5 Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq. Noor S. Sagheer https://orcid.org/0000-0003-1167-592X Faezah Hamad Almasoudy https://orcid.org/0009-0002-7739-3327 Department of Animals Production, College of Agriculture, Kerbala University, Kerbala, Iraq. Manar Hamza Bashaa https://orcid.org/0000-0002-8824-9112 Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq. The present time, Sign language is very important for people who suffer from hearing loss or who cannot speak. Normal humans tend to disregard the significance of signal language, which is a mere supply of communique to mute and deaf societies. So, this study proposes a developed model for sign Language Recognition for Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then set the algorithm by developing programming on Open-CV, using Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, and each one of them included 1500 images incorporating hand gestures in at-variance environments. The data set was divided into a training section with a percentage of 70 %, a section for testing with a percentage of 20 %, and a section for validation with a percentage of 10 %. The results show that the suggested model achieved an accuracy rate of 94.8%, and it has proven its effectiveness and success, especially after being tried and tested by several users and obtaining their comments and feedback. 04 30 2024 18 23 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijitee.E9844.13050424 https://www.ijitee.org/portfolio-item/E984413050424/