Sign Language Detection using Python

Abstract

Communication barriers faced by hearing-impaired and speech-impaired individuals remain a significant social challenge, particularly in daily interactions with non-sign-language users. Sign language serves as an effective medium for communication; however, the lack of widespread understanding limits its practical use. To address this issue, this project proposes a real-time sign language detection system using Python and deep learning techniques to automatically recognize hand gestures and convert them into readable text. The primary objective of the proposed system is to enable efficient and accurate interpretation of sign language gestures using commonly available hardware and open-source software tools. The system utilizes a webcam to capture real-time video streams, which are processed frame by frame using OpenCV. Image preprocessing techniques such as resizing, normalization, and noise reduction are applied to enhance image quality and improve detection performance under varying lighting and background conditions. A YOLO-based object detection model is employed to localize hand regions within each frame due to its high detection speed and suitability for real-time applications. The detected hand gestures are then classified into corresponding sign language symbols using a trained deep learning model. The recognized gestures are translated into meaningful textual output and displayed on the screen in real time, allowing seamless interaction between hearing-impaired users and normal users. The proposed approach demonstrates reliable performance with acceptable accuracy and low latency, making it suitable for real-world applications. Experimental observations indicate that the system can effectively recognize common hand gestures while maintaining smooth real-time processing on standard laptop configurations. Overall, the proposed sign language detection system offers a cost-effective, user-friendly, and socially impactful solution for improving accessibility and inclusivity. The system can be further extended by incorporating speech synthesis, supporting dynamic gestures, and expanding the dataset to include more sign language vocabularies for enhanced accuracy and scalability.