TY  - EJOU
AU  - Das, Khushal 
AU  - Abid, Fazeel 
AU  - Rasheed, Jawad 
AU  - Kamlish, 
AU  - Asuroglu, Tunc 
AU  - Alsubai, Shtwai 
AU  - Soomro, Safeeullah 

TI  - Enhancing Communication Accessibility: UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 141
IS  - 1
SN  - 1526-1506

AB  - Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language with limited resources. We conducted experiments using two datasets containing 1500 and 78,000 images, employing a methodology comprising four modules: data collection, pre-processing, categorization, and prediction. To enhance prediction accuracy, each sign image was transformed into a greyscale image and underwent noise filtering. Comparative analysis with machine learning baseline methods (support vector machine, Gaussian Naive Bayes, random forest, and k-nearest neighbors’ algorithm) on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN, achieving an accuracy of 0.95. Additionally, our model exhibited superior performance in Precision, Recall, and F1-score evaluations. This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.
KW  - Convolutional neural networks; Pakistan sign language; visual language

DO  - 10.32604/cmes.2024.051335