Word level american sign language video cllassification using ConvLSTM, LRCN, CNN
This repository contains the "WLASL Recognition", employing the WLASL dataset descriped in "Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison" by Dongxu Li.
After the World Health Organization, Over 5% of the world's population – or 430 million people - are deafs. Our PFA was about the creation of a digital assistant system for deafs that translate Sign language to Text in real time, and from text or audio to Sign language using deep learning methods : CNN, ConvLSTM and LRCN.
This repository is a comparison between all the Methods
WLASL is the largest video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. We hope WLASL will facilitate the research in sign language understanding and eventually benefit the communication between deaf and hearing communities.
The dataset used in this project is the "WLASL" dataset and it can be found here on Kaggle
Download the dataset and place it in data/ (if you place it in other location try to change data import location)
The end results of the project looks like this.
The conversion of Computer to Spoken Language.
https://drive.google.com/file/d/1-PZdUJep_hwGMORyjcyAiHX0KtiLvufc/view?usp=sharing