Signapse is an open source software tool for helping everyday people learn sign language for free!
A real-time American Sign Language (ASL) detection system using computer vision and deep learning. This project uses a combination of OpenCV, MediaPipe, and TensorFlow to detect and classify ASL hand signs from camera input. The system can recognize a wide range of ASL characters, and can be used to facilitate communication for sign language users.
Creating an Indian Sign Language detection model using YOLOv5 involves training a model to recognize and locate different signs in images or videos.
Signfy is a Video Chat app that incorporates sign language translation to bridge the communication gap between the deaf and hearing communities.
Hand Gesture Recognition for Sign language Detection deployed along a web application that recognizes through which one can translate hand gestures into commands or text using a webcam.
Aplicación que permite traducir lengua de señas a audio y texto.
Realtime Sign Language Application that detects signs based on object detection model.
Shady Elkholy's graduation project, Arab Open University, Computer science
Mobile Development team will handle the wireframing and designing UI/UX and implement it to the application. The application it self will send a request in the form of an object that is captured and then it will be sent to be processed in the model made by the ML team and deployed by the CC team.
This repository is part of the "learnSign" project that is under development and will be submitted for the award of Degree of Bachelor of Technology in Computer Science and Engineering from Rajasthan Technical University, Kota (India).
This project is designed for American Sign Language (ASL) detection using OpenCV and MediaPipe Hand module.
A realtime translator app for sign language using Mediapipe Hands solutions and LSTM TFLite model.
Sign Language Recognition System is an AI-powered application that enables real-time sign language recognition using MediaPipe and an MLP model. It captures hand gestures, extracts landmark features, and predicts sign language letters dynamically. The project also explores MobileNetV2 and aims to expand into Text-to-Sign Language generation.
Sign Language Detection on Arduino Nano 33 BLE Sense
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