SIGNLINGUA: A MOBILE APPLICATION SIGN LANGUAGE TRANSLATOR USING MACHINE LEARNING

Abstract

This study developed SignLingua, a mobile application that translates American Sign Language (ASL) alphabets into text using TensorFlow Lite, while also serving as a learning platform for both ASL users and non-users. It addresses key communication barriers faced by the deaf community, including limited sign language proficiency among the hearing population and lack of accessible educational tools.

Using a mixed-methods approach, the study involved 30 randomly selected participants from Lingayen I Central School. Data were gathered through interviews and ISO 9126-1-based survey questionnaires. The application was developed using the Agile methodology, ensuring iterative feedback and functionality refinement.

Findings identified major communication challenges and demonstrated the app’s effectiveness. The system received an overall acceptability rating of 4.67, classified as “Very Highly Accepted” across all software quality criteria—functionality, reliability, usability, efficiency, maintainability, and portability.

The results indicate that SignLingua is a reliable and inclusive communication tool that supports real-time ASL translation and promotes accessible learning. It offers practical benefits to both deaf and hearing users and provides a foundation for future advancements in sign language technology.