Abstract
Abstract: The sign language Recognition System is a technology that bridges the gap between the deaf people and the hearing world. Sign language is a vital mode of expression for deaf people, yet effective communication remains a challenge. This project aims to develop a robust Sign Language Recognition System capable of accurately translating sign language gestures into text. The system utilizes a deep learning approach, specifically convolutional neural networks (CNNs) to analyze video input of sign language and then generate the corresponding output. The project involves data collection, pre-processing, segmentation, feature extraction, model training, and evaluation. The proposed SLR system has the potential to enhance. Communication and accessibility for deaf individuals, promoting inclusivity and improving their quality of life. The project represents a comprehensive exploration of both technical and ethical aspects in the realm of computer vision and deep learning applications.
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