An Image-Based Deep Learning Framework for Hand Gesture Sign Language Recognition

Hand gesture - based sign language recognition using computer vision has significant potential to enhance communication for individuals with hearing and speech impairments. Sign language is a visual mode of communication that relies on hand gestures, facial expressions, and body movements to convey meaning. Conventional communication methods often require human interpreters or pre-recorded visual aids, which may be time-consuming, costly, and not always available. This paper presents an image-based deep learning framework for hand gesture sign language recognition, designed to translate sign gestures into corresponding text and speech output. The proposed framework captures hand gesture images through a camera, applies image preprocessing techniques such as grayscale conversion, image enhancement, histogram equalization, and edge detection, and extracts meaningful features from the processed images. These features are then used to train a deep learning–based classification model capable of accurately recognizing sign language gestures. The recognized gestures are converted into readable text and synthesized speech using text-to-speech technology, enabling effective interaction between hearing-impaired users and the general public. The experimental results demonstrate that the proposed framework achieves reliable recognition accuracy and provides an efficient assistive communication solution for real-world applications such as train ticket booking systems.