Real-time-sign-language-recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone.

Dataset

The dataset is made manually by running the Data_collection jupyter notebook that collects images from your webcam for all the signs mentioned above in the American Sign Language. Data has been collected for 24 alphabets only (not collected for 9=J and 25=Z because of gesture motions). For each alphabet 15 images with there annotations has been collected from which 10 images with there annotations has been used for training the ssd_mobnet model and 5 images with there annotations has been used for testing our ssd_mobnet model (I have only uploaded the test images with there annotations similarly train images were also used to train the ssd_mobnet).

The image annotations is done with the help of LabelImage file which you can use by simply git cloning the following- Link

Real time sign language recognition

Related tags

Overview

Real-time-sign-language-recognition

Dataset

Testing

Resources used

Owner

Mohit Kaushik

Automatic self-diagnosis program (python required)Automatic self-diagnosis program (python required)

This repo is for segmentation of T2 hyp regions in gliomas.

Python Fanduel API (2021) - Lineup Automation

OrienMask: Real-time Instance Segmentation with Discriminative Orientation Maps

Machine Unlearning with SISA

Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

Visualizer using audio and semantic analysis to explore BigGAN (Brock et al., 2018) latent space.

A3C LSTM Atari with Pytorch plus A3G design

clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Multivariate Time Series Transformer, public version

VIsually-Pivoted Audio and(N) Text

Weakly-supervised object detection.

Framework for abstracting Amiga debuggers and access to AmigaOS libraries and devices.

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Normalization Matters in Weakly Supervised Object Localization (ICCV 2021)

Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation