Hand Sign Detection Using Python And Deep Learning

Sign-based gestures form a rich, non-verbal mode of interaction and hold particular value for people who are deaf, hard of hearing, or speech-impaired and who use sign language as part of everyday communication. This work describes a real-time handgesture recognition pipeline that classifies gestures captured live from a webcam by combining classical computer-vision techniques with a deep convolutional network. Video frames are read and processed using OpenCV, while hand localization and the extraction of twentyone skeletal landmark points are handled by the CVZone library, which itself wraps Google's MediaPipe hand-tracking solution. Recognition of the gesture class is carried out by a TensorFlow classifier. Prior to inference, each frame is cropped around the detected hand, resized to a fixed square canvas, padded against a neutral background, and adjusted so that the original aspect ratio of the hand is preserved. Evaluation across unit, integration, and end-toend system tests confirmed stable, low-latency operation under everyday indoor lighting. Beyond its immediate accessibility use case, the pipeline is well suited to touch-free interfaces more broadly, with possible extensions into education, robotics, and ambient/smart-device control. The paper closes by outlining planned extensions covering continuous (dynamic) gesture tracking, broader multilingual signlanguage coverage, and porting the system to mobile and IoT targets.

Index Terms—Hand Gesture Recognition, Computer Vision,

MediaPipe, TensorFlow, Deep Learning, OpenCV, Sign Language, MobileNetV2