Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

Overview

PR's Welcome made-with-python

RealTime Sign Language Detection using Action Recognition

Approach

Real-Time Sign Language is commonly predicted using models whose architecture consists of multiple CNN layers followed by multiple LSTM layers. However , the accuracy of these state of the art models is pretty low. On the other hand, this approach , Mediapipe Holistic with LSTM Model gives a much better accuracy. This approach produced better results with very less amount of data . Since this model trained on fewer parameters, it trained much faster thus resulting in lesser computation time.

Project

This project is divided into two parts:

  1. Keypoints extraction using MediaPipe Holistic
  2. LSTM Model trained on these keypoints to predict realtime sign language using video sequences.

Dataset

Data is collected using MediaPipe Holistic for 3 actions :

  • Hello
  • Thanks
  • I Love You

30 frames have been collected for each action and 30 sequences for each frame have been collected from real time actions using Computer Vision and MediaPipe Holistic. For each sequence , 1662 keypoints have been extracted.

  • Face Landmarks - 468*3
  • Pose Landmarks - 33*4
  • Left Hand Landmarks - 21*3
  • Right Hand Landmarks - 21*3

                   

The dataset can be accessed from the Feature_Extraction Folder.

Model

LSTM Model is trained using the extracted keypoints from the Feature_Extraction folder and later used for real time predictions.

         

The Weights of the model are saved in the lstm_model.h5 file.

How to Use

  • Clone the repository using :

      $ git clone https://github.com/rishusiva/Pose-Network
    
  • Install the requirements using:

      $ cd Pose-Network/
      $ pip install -r requirements.txt
    
  • To Predict Sign Languages in Real Time , run :

      $ cd Pose-Network/Code
      $ python3 realtime_testing.py
    

Results

  • Our LSTM Model, after training for only 100 epochs, has an accuracy of 70%
  • It produced an accuracy score of 1.0 on a test set of 5 images.
  • Our Trained LSTM Model is then used for real time testing.

Prediction Results:

         

Author

  • Rishikesh Sivakumar

ForTheBadge built-with-love by Rishikesh Sivakumar

Owner
Rishikesh S
Currently pursuing Computer Science Engineering. Working towards the upliftment of the machine learning community. Aspiring ML and DL engineer.
Rishikesh S
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

VAE with Volume-Preserving Flows This is a PyTorch implementation of two volume-preserving flows as described in the following papers: Tomczak, J. M.,

Jakub Tomczak 87 Dec 26, 2022
A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

LegoNet This code is the implementation of ICML2019 paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters Run python train.py You c

YangZhaohui 140 Sep 26, 2022
A fast Protein Chain / Ligand Extractor and organizer.

Are you tired of using visualization software, or full blown suites just to separate protein chains / ligands ? Are you tired of organizing the mess o

Amine Abdz 9 Nov 06, 2022
[ACM MM 2021] Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)

Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation) [arXiv] [paper] @inproceedings{hou2021multiview, title={Multiview

Yunzhong Hou 27 Dec 13, 2022
Mengzi Pretrained Models

中文 | English Mengzi 尽管预训练语言模型在 NLP 的各个领域里得到了广泛的应用,但是其高昂的时间和算力成本依然是一个亟需解决的问题。这要求我们在一定的算力约束下,研发出各项指标更优的模型。 我们的目标不是追求更大的模型规模,而是轻量级但更强大,同时对部署和工业落地更友好的模型。

Langboat 424 Jan 04, 2023
Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

StyleGAR TODO: add arxiv link Implementation of Inverting Generative Adversarial Renderer for Face Reconstruction TODO: for test Currently, some model

155 Oct 27, 2022
TorchX: A PyTorch Extension Library for More Efficient Deep Learning

TorchX TorchX: A PyTorch Extension Library for More Efficient Deep Learning. @misc{torchx, author = {Ansheng You and Changxu Wang}, title = {T

Donny You 8 May 28, 2022
pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

Unofficial implementation: MoCo: Momentum Contrast for Unsupervised Visual Representation Learning (Paper) InsDis: Unsupervised Feature Learning via N

Zhiqiang Shen 16 Nov 04, 2020
Código de um painel de auto atendimento feito em Python.

Painel de Auto-Atendimento O intuito desse projeto era fazer em Python um programa que simulasse um painel de auto atendimento, no maior estilo Mac Do

Calebe Alves Evangelista 2 Nov 09, 2022
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

Oliver Hahn 1 Jan 26, 2022
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

1.4k Jan 04, 2023
eXPeditious Data Transfer

xpdt: eXPeditious Data Transfer About xpdt is (yet another) language for defining data-types and generating code for serializing and deserializing the

Gianni Tedesco 3 Jan 06, 2022
Implementation of ICCV21 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers

Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR

twang 113 Dec 27, 2022
Python scripts for performing stereo depth estimation using the HITNET Tensorflow model.

HITNET-Stereo-Depth-estimation Python scripts for performing stereo depth estimation using the HITNET Tensorflow model from Google Research. Stereo de

Ibai Gorordo 76 Jan 02, 2023
Reinforcement Learning via Supervised Learning

Reinforcement Learning via Supervised Learning Installation Run pip install -e . in an environment with Python = 3.7.0, 3.9. The code depends on MuJ

Scott Emmons 49 Nov 28, 2022
ncnn is a high-performance neural network inference framework optimized for the mobile platform

ncnn ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployme

Tencent 16.2k Jan 05, 2023