{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-recognition-for-arabic-alphabet","title":"Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset","arxiv_id":null,"date":"2024-03-11","proceeding":"Journal of Computer and Communications 2024 3","authors":["Rabie El Kharoua","Xiaoming Jiang"],"abstract":"This paper introduces a Convolutional Neural Network (CNN) model for Arabic Sign Language (AASL) recognition, using the AASL dataset. Recognizing the fundamental importance of communication for the hearing-impaired, especially within the Arabic-speaking deaf community, the study emphasizes the critical role of sign language recognition systems. The proposed methodology achieves outstanding accuracy, with the CNN model reaching 99.9% accuracy on the training set and a validation accuracy of 97.4%. This study not only establishes a high-accuracy AASL recognition model but also provides insights into effective dropout strategies. The achieved high accuracy rates position the proposed model as a significant advancement in the field, holding promise for improved communication accessibility for the Arabic-speaking deaf community.","url_abs":"https://www.scirp.org/journal/paperinformation?paperid=131670","url_pdf":"https://www.scirp.org/pdf/jcc_2024030814564567.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":null,"task_name":"Position"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[{"slug":"no-background-rgb-arabic-alphabets-sign","name":"No Background RGB Arabic Alphabets Sign Language Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-no-background-rgb","task":"Image Classification","dataset":"No Background RGB Arabic Alphabets Sign Language Dataset","model":"ArabSignNet","rank_in_archive_order":1,"of":1,"metrics":{"Validation Accuracy":"97.4"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-rgb-arabic-alphabet","task":"Image Classification","dataset":"RGB Arabic Alphabet Sign Language (AASL) dataset","model":"ArabSignNet","rank_in_archive_order":1,"of":1,"metrics":{"Validation Accuracy":"97.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}