Published Online:April 2026
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS040126
DOI:10.71329/IUPJCS/2026.20.2.7-20
Author Name:Samarth Mishra, Devraj Mishra, Nikita Singh, Ashish Pandey and Shubham Kumar Singh
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:7-20
Language barriers significantly limit the scope of people suffering from hearing and speech impairment, hindering them from being able to communicate with the general community. Indian Sign Language (ISL), the only means of communication for the majority of this community in India, has no extensive digital translating capabilities. This paper proposes an ISL translator based on deep learning that can correctly interpret and transcribe hand signs to textual form, overcoming the communication barrier. The proposed system recognizes the ISL alphabet (A-Z) and numbers (1-9) correctly. Besides, the system includes edge detection and tracking landmarks, solving problems due to varying lighting, and partial occlusions. The system also develops an application over the web, so it has accessibility advantages. Through computer vision, coupled with the capabilities of deep learning, the study contributes to an inclusive digital communications platform, empowering people who depend on sign language.
Communication, a basic task in human interaction, allows one to convey their thoughts, feelings, and ideas to others.