Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others
Soliman, A., Khattab, M., Ahmed, A. (2022). Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others. EKB Journal Management System, 31(57), 1-30. doi: 10.21608/qarts.2023.192742.1617
Amr Mohmed Soliman; Mahmoud M. Khattab; Abdelmoty M. Ahmed. "Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others". EKB Journal Management System, 31, 57, 2022, 1-30. doi: 10.21608/qarts.2023.192742.1617
Soliman, A., Khattab, M., Ahmed, A. (2022). 'Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others', EKB Journal Management System, 31(57), pp. 1-30. doi: 10.21608/qarts.2023.192742.1617
Soliman, A., Khattab, M., Ahmed, A. Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others. EKB Journal Management System, 2022; 31(57): 1-30. doi: 10.21608/qarts.2023.192742.1617

Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others

مجلة کلية الآداب بقنا
Article 26, Volume 31, Issue 57, October 2022, Page 1-30  XML PDF (1.06 MB)
Document Type: المقالة الأصلية
DOI: 10.21608/qarts.2023.192742.1617
View on SCiNiTO View on SCiNiTO
Authors
Amr Mohmed Soliman email orcid 1; Mahmoud M. Khattab* 2; Abdelmoty M. Ahmed* 2
1Special Education Dept, College of Education, King Khalid University, Abha, Saudi Arabia
2College of Computer Science, King Khalid University, Abha, Saudi Arabia
Abstract
The predominant method of communication for hearing-impaired and deaf people is still sign language. It is a carefully constructed hand gesture language, and each motion denotes a certain meaning. The purpose of this paper is to create a system for Arabic Sign Language automatic translation. The proposed Arabic sign to Text System consists of five primary stages and serves as a translator for deaf and dumb persons and normal people to improve communication. This system depends on building a two datasets image features for Arabic sign language gestures alphabets from two resources: the Arabic Sign Language dictionary and gestures from different signers' humans. It also uses gesture recognition techniques, which lets the user interact with the outside world. Video and images capture, Video and images processing, Hand Signs construction, classification, and finally text transformation and interpretation. In this paper, we use a set of appropriate features in step hand sign construction and classification of based on different classification algorithms such as (KNN, MLP and C4.5) and compare these results to get better classifier. This system offers a novel technique of hand detection that detects and extracts hand gestures of Arabic Sign from Image or Video..
Keywords
Arabic Sign Language; Hand Gesture; Features Extraction; Hand Detection
Statistics
Article View: 101
PDF Download: 82