This project develops a Sign Language Detection system to bridge the communication gap between hearing individuals and the deaf or hard-of-hearing community. Using machine learning and computer vision, the system detects and classifies American Sign Language (ASL) signs in real time, promoting accessibility.

The project is about translating American Sign Language into English language. It uses Computer Vision and Deep Learning to predict the ASL alphabet and forms sentences on the basis of prediction. It uses text to speech to convert the predicted word into speech. The project was implemented at MNNIT Hack36 Allahabad Hackathon.

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.

This project is aimed at detecting American Sign Language (ASL) alphabets in real-time using computer vision. The system utilizes OpenCV for image processing, MediaPipe for hand detection, and a Random Forest classifier from scikit-learn for alphabet recognition.

Project focused on the detection and interpretation of American Sign Language (ASL) using deep learning and computer vision techniques. The research integrates MediaPipe, a framework developed by Google for hand tracking, with LSTM (Long Short-Term Memory) neural networks, to create a system capable of recognizing sign language gestures.

A project leveraging computer vision and machine learning techniques to interpret American Sign Language (ASL) hand gestures in real-time. Developed using the MediaPipe library for hand landmark detection and TensorFlow/Keras for gesture classification. Enhances accessibility and communication for the deaf and hard of hearing community.

Real-time American Sign Language detection system using CNN, OpenCV, MediaPipe, and PyTorch for computer vision-based sign language recognition

American Sign Language Detection system using computer vision techniques like hand detection, face detection and classification techniques like deep neural networks.

This project is focused on real-time American Sign Language (ASL) gesture detection using computer vision and machine learning. It uses OpenCV and MediaPipe for hand tracking, and it leverages the power of the ResNet50 architecture for sign language recognition.

Sign language detection using American Sign Language (ASL) is an advanced computer vision and deep learning application that recognizes hand gestures and interprets them as ASL characters in real time. This technology typically involves capturing video or image data of hand movements.

Tivon is a real-time American Sign Language (ASL) recognition web app using computer vision and deep learning. It offers live sign detection via webcam, an interactive learning interface, progress tracking, and privacy-focused design to bridge communication gaps and support ASL learners.

Developed an American Sign Language (ASL) detection system using Convolutional Neural Networks (CNN) in TensorFlow. Focused on accurate recognition of ASL alphabets and special gestures through image classification techniques. A practical application of deep learning and computer vision.

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American Sign Language detection using Computer Vision

Live American Sign Language Detection using Computer Vision (YOLOv8 model)

Easy Hand Sign Detection using OpenCV | American Sign Language ASL | Computer Vision

american sign language detection using deep Learning CNN and computer vision

Computer Vision and Deep learning-based American sign language detection system using OpenCV and Tensoflow.

In this project we use computer vision to perform Image Detection for American Sign Language

Built an American Sign Language (ASL) hand sign detection system using computer vision with cvzone for real-time gesture recognition.

Real-Time Sign Language Detection Real-time American Sign Language (ASL) recognition using computer vision and machine learning — turn your webcam into a sign interpreter!

ASL Classification: A machine learning project for American Sign Language (ASL) classification using computer vision and hand landmark detection.

The American Sign Language Detection and Translation System is an AI project for translating American Sign Language (ASL) gestures into text in real-time using computer vision and deep learning.

ASL Detection – American Sign Language Recognition A machine learning project that detects and translates ASL hand gestures into text using computer vision.

This model detects the American Sign Language(ASL) signs real-time by using Computer Vision and Deep Learning .This system combines computer vision techniques with deep learning architectures, specifically LSTMs, to recognize American Sign Language signs in real-time, leveraging Google's MediaPipe Solutions for hand landmark detection .

A real-time American Sign Language (ASL) recognition system focusing on hand detection and gesture interpretation using computer vision and machine learning techniques.

Sign detection is the process of using computer vision and artificial intelligence to recognize and interpret hand gestures, body movements, and facial expressions used in sign languages such as ASL (American Sign Language), BSL (British Sign Language), and others.

This project is an American Sign Language (ASL) detection system that recognizes hand gestures using computer vision and machine learning and converts them into speech

I have did a machine learning project on hand sign detection using computer vision. The hand sign detection is based on the American Sign Language. I have used libraries like cvzone,mediapipe and tensorflow. Finally trained the models using teachable machine.