Authors : Mohammad Sanabil; Mohamed Sidan E. K.; Fathima Hiba P. C.; Muhammed Shehin K. T.; Hiba Thasni K. P.; Aswathi P.

Volume/Issue : Volume 11 - 2026, Issue 7 - July

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Google Scholar : https://tinyurl.com/bdz5u2vf

Scribd : https://tinyurl.com/2h4kuftm

DOI : https://doi.org/10.38124/ijisrt/26jul822

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Abstract : To overcome the major communication issues faced by deaf and speech-impaired individuals, a new deep learningbased Indian Sign Language (ISL) translation system is proposed. It is difficult for hearing-impaired individuals to communicate effectively without interpreters. The proposed system translates ISL gestures into English text and audio output and converts English text into ISL gesture sequences. MediaPipe is used for real-time hand landmark detection and a Convolutional Neural Network (CNN) model is used for gesture classification. The system reduces communication barriers and improves accessibility. These are the main achievements of the project.

Keywords : Indian Sign Language, Deep Learning, CNN, MediaPipe, Gesture Recognition.

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