Sign Language Translator using Graph Convolution Networks (GCN)
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Updated
May 28, 2024 - Jupyter Notebook
Sign Language Translator using Graph Convolution Networks (GCN)
This project involves creating a real-time sign language detection system using CNNs to translate sign language gestures into text. It aims to improve communication accessibility for the hearing-impaired by accurately recognizing and displaying sign language gestures from live video input in real-time.
Code for the demo of the VGT-NL dictionary at Dag Van De Wetenschap 2023 and other events.
This project focused on developing a model for recognizing hand gestures in sign language using deep learning. I collected Sign MNIST dataset and trains a convolutional neural network (CNN) model to classify sign language gestures.
Sanket is a real-time sign language recognition application. It's designed to recognize a variety of sign language gestures, making communication easier for those who use sign language.
Teaching computers to understand sign language! This project uses image processing to recognize hand signs, making technology more inclusive and accessible.
it able to detect ten type USA sign language. which is Okay, Peace, Thumbs up, Thumbs down, Call me, Stop, I Love You, Hello, No, Smile.
Using yolo-v8 to train on custom dataset for sign language recognition
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.
We have trained CNN (Convolutional Neural Network) algorithm to predict sign language. There are also pretrained models which also used in this model.
Sign Language to Speech and vice-versa converter
American Sign Language Recognition
This repo contains the code for sign-language-recognition as part of our final year project.
This web-based app detects and interprets sign languages into English words in real-time in order to help speech-impaired individuals communicate with others more easily.
Task 1: Sign Language Classification using machine learning at SYNC INTERN'S.
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.
packages needed to work this project are as follows :- OpenCV , Tensorflow , Pyenchant , Mediapipe , keras , numpy , gtts , tkinter
ASL Letter Classification: Using a CNN to classify American Sign Language (ASL) letter images with high accuracy.
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