Using YOLOv5 and opencv to perform (live) object detection for several hand gestures.

The Sign Language Detection System is an end-to-end deep learning application that recognizes sign language hand gestures using the YOLOv5 real-time object detection model. It processes images or live camera feeds and runs through a Flask web server, with automated deployment to AWS using Docker and a GitHub Actions–based CI/CD pipeline.

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This repository contains code for a Gesture Recognition and Object Detection System using YOLOv5 and a custom-trained model. The system can detect hand gestures and objects in real-time using a webcam or process images.

Built a real-time sign language detection system using YOLOv5, enabling automatic recognition of hand gestures from webcam or image input. The system was trained on a custom dataset of sign gestures and deployed using Flask as a web interface.

Sign Language Detection is a project aimed at recognizing and interpreting hand gestures from sign language. This end-to-end solution employs the YOLOv5 object detection model to identify sign language phrases such as "Hello," "I love you," "Yes," "No," and "Please." The model can help bridge communication gaps and facilitate better understanding.

Sign Language Detection is a project aimed at recognizing and interpreting hand gestures from sign language. This end-to-end solution employs the YOLOv5 object detection model to identify sign language phrases such as "Hello," "I love you," "Yes," "No," and "Please." The model can help bridge communication gaps and facilitate better understanding.

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Gesture-based object detection using YOLOv5 and MediaPipe Hands

A computer vision system that detects and recognizes sign language hand gestures in real time using the YOLOv5 object detection model.

Real-time hand tracking and gesture detection system using YOLOv5, PyTorch, and OpenCV. Optimized for high-speed inference and precise bounding box accuracy.

Empowering seamless communication: Real-time ASL Detection using YOLOv5. Our Python-based project bridges the gap between ASL users and non-users through instant, accurate hand gesture interpretation.

GestoAI is an object detection model using YOLOv5 to recognize Italian hand gestures in real-time. It enhances nonverbal communication and has applications in cross-cultural interaction, education, and immersive experiences.

The project uses YOLOv5 for real-time hand gesture detection in videos, converting gestures into corresponding sign language text. It helps the hearing-impaired by translating sign language into a digital format using computer vision techniques, improving accessibility and communication.

eal-time Sign Language Detection System using YOLOv5 and Computer Vision — an AI-powered model that accurately recognizes and translates hand gestures into text, helping bridge the communication gap between sign language users and others.