Sign-Speak: Real-Time Continuous Indian Sign Language Translator

Abstract: Communication between users of Indian Sign Language (ISL) and people who do not understand sign language can be challenging. Computer vision and machine learning provide opportunities to develop assistive systems that recognize visual gestures and present their predicted meanings in accessible formats. This paper presents Sign-Speak, a web-based sign-language recognition and translation application that integrates real-time camera input, hand-landmark visualization, gesture classification, text output, speech controls, and uploaded-video processing. The application provides a user interface for viewing the camera feed, observing the predicted sign and confidence score, and accessing translation results. It also supports uploading video files in MP4, AVI, and MOV formats, subject to the interface's displayed 50 MB limit. The system uses a trained machine-learning model within a Python-based application. The implementation combines computer-vision processing and a web interface to provide an accessible way to experiment with sign recognition. The application was launched and its interface and workflows were observed during project testing. Quantitative accuracy, processing speed, and generalization performance must be established through controlled experiments before they can be reported as measured results. The proposed system serves as a prototype for assistive
evaluation.

Keywords: Indian Sign Language, Sign Language Recognition, Computer Vision, Deep Learning, Hand Gesture Recognition, LSTM, Real-Time Processing, Text-to-Speech, Assistive Technology.