A Hijaiyah Letters Sign Language Recognition Approach utilizing Deep Learning
Received: 18 January 2025 | Revised: 14 February 2025, 24 February 2025, and 7 March 2025 | Accepted: 9 March 2025 | Online: 4 June 2025
Corresponding author: Dadang Priyanto
Abstract
Sign language was created as a means of communication for individuals with hearing impairments. However, learning sign language is not easy and requires significant effort and dedication, posing challenges for deaf children. Mastering sign language, particularly the Arabic alphabet (Hijaiyah), is particularly demanding and necessitates specialized learning tools to enhance comprehension. The limited number of sign language teachers and the complexity of the learning process pose significant challenges at Sekolah Luar Biasa Negeri (SLBN) 1 Mataram. This study explores the use of a Logistic Regression (LR) algorithm to assist deaf students in learning Hijaiyah sign language. The dataset comprises 28 Hijaiyah letters. The process begins with data preprocessing to detect hands using hand detection landmarks, followed by the classification of the 28 Hijaiyah sign language gestures using LR. The study's results indicate that the proposed model had an accuracy rate of 96% in recognizing Hijaiyah sign language, demonstrating the algorithm's effectiveness for this application.
Keywords:
sign language, logistic regression, Hijaiyah letters, deaf, deep learningDownloads
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