A simple ASL (American Sign Language) alphabet detection using TensorFlow and Handpose model

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.

To detect American Sign Language in real-time.

Real-time American Sign Language (ASL) letters detection, via PyTorch, OpenCV, YOLOv5, Roboflow and LabelImg 🤟

Real-time ASL sign language detector using MediaPipe hand landmarks + XGBoost — 98.43% accuracy, skin-tone agnostic, no CNN

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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.

🧏 Detection of the alphabets in American Sign Language (ASL).

Performance Analysis for ASL detection using Yolo-v5 object detection algorithm

ASL to Text and Speech converts sign language to text and speech in real-time. Using computer vision and ML, it recognizes hand gestures from webcam input and translates them into spoken words, making communication accessible for the hearing impaired. Features include real-time detection, text conversion, and speech synthesis.

Machine Learning Based Detection Of ASL(American Sign Language) Gestures

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American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch

A real-time hand gesture recognition app that translates ASL gestures into spoken words

A Python ASL detection script utilizing a TensorFlow image classification model trained from scratch. It is tailored to recognize American Sign Language (ASL) alphabet letters from live video streams, and provides documentation covering the neural network architecture, installation, dataset details, training procedures, and real-time detection.

ASL Object Detection Project: A computer vision project that utilizes deep learning techniques to detect and recognize American Sign Language (ASL) gestures in real-time.

ASL learning and practicing React Native mobile application using Tensorflow.js handpose and fingerpose models for real-time gesture detection

Applied SSD integrated with MobileNet model for object (sign gestures) detection and recognition and the model is trained using Transfer Learning, with the aim to develop a web app for real-time ASL recognition from user input & then to generate text in English.

This project implements Vietnamese Sign Language (VSL) and American Sign Language (ASL) recognition using the YOLOv8 object detection framework. The goal is to build a system capable of detecting and classifying hand signs in real time, supporting communication for the deaf and hard-of-hearing community.

Detection of ASL Letters using MRTK + Hololens2

This is a Sign language detection model that can accurately recognize and interpret the gestures and movements of hands used in American Sign Language (ASL).

This program will use gesture detection to help identify common ASL gestures as well as alphabets, translating them into sentences.

Exploring strategies for Sign Language Recognition task using ASL Dataset: DL Classification, Object Detection and Landmarks Classification

This project is a machine learning-based Sign Language Detection System designed to identify alphabets from American Sign Language (ASL). The project consists of various Python scripts for different stages of the pipeline, including data collection, preprocessing, model training, and deploying a web-based application for real-time detection.

手语识别系统源码分享[一条龙教学YOLOV8标注好的数据集一键训练_70+全套改进创新点发刊_Web前端展示]

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Detecting American Sign Language on live video using Resnet50

Simple Hand Sign Detection Using Python and TensorFlow Libraries (i created only A,B,C Letters) i used Teachablemachine to Create the model, Accuracy is not 100%

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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.