https://github.com/tirtharajsinha/asl-classifier
A two staged approach to Classify ASL
https://github.com/tirtharajsinha/asl-classifier
asl asl-classifier deep-learning python
Last synced: 8 months ago
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A two staged approach to Classify ASL
- Host: GitHub
- URL: https://github.com/tirtharajsinha/asl-classifier
- Owner: tirtharajsinha
- License: gpl-3.0
- Created: 2023-01-11T19:35:25.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-11T15:31:06.000Z (over 2 years ago)
- Last Synced: 2025-01-04T21:28:09.043Z (9 months ago)
- Topics: asl, asl-classifier, deep-learning, python
- Language: Jupyter Notebook
- Homepage: https://tirtharajsinha.github.io/ASL-Classifier/
- Size: 23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Two-Stage ASL Detection Architecture: A Hand Sign Languages Detection Scheme
> A new two staged approach to Classify Hand sign (American Sign Language)
- Domain : AI/ML in Support of Human Cognition
- State : Review phase
## Aim
The aim is to develope a method to classify hand sign from image efficiently and building a **_Realtime sign detector Application_**. Classical Hand sign detector models train directly from images and I have discovered that it negitively affects the realtime detection accuracy.
These are factors are :-
- Hand side [Left/Right]
- Skin color and birthmarks on hand
- Hand distance from camara
- Camara quality
- Hand angel
- Rapid movement
- Ambient lighting
- Background noise, color, movement
We have tried to eliminate this limitation with a different 2 staged approach.
## Requirements
1. Python >3.8
2. Jupyter Notebook or Lab
3. git
## Setup
- Clone the Repo
```
git clone https://github.com/tirtharajsinha/ASL-Classifier.git
cd ASL-Classifier
```
- Get the [dataset](https://www.kaggle.com/datasets/ayuraj/asl-dataset?resource=download)
- Setup and active the virtual environment (Optional)
- Setup the virtual environment (Optional/Recommended)
```
pip install virtualenv
virtualenv venv
./venv/Scripts/activate
```
- Install the python dependencies (Inside venv or on root)
```
pip install -r requirements.txt --user
```
## Download the Dataset
Kaggle Link :-
[Dataset](https://www.kaggle.com/datasets/ayuraj/asl-dataset?resource=download)
## Run the general ASL detection algorithm
- change the dataset path variable `PATH` with your local path.
- Run the `ASL_dataset generator.ipynb`
- Run the `ASL_keypoint_model_trainer.ipynb`
- Run the `ASL_keypoint_detector.ipynb`
- Run the `Model_tester.ipynb`
## Run the realtime ASL detection Application
```
python trackOnCam.py
```
## upstream the local repository with remote repository
```
git remote add upstream https://github.com/tirtharajsinha/ASL-Classifier.git
git fetch upstream
git checkout main
git merge upstream/main
```
## reset repo
```
git reset --hard origin/main
```
## Evaluation Reasult
### Hardware (Tested)
- Device : Dell inspiron 3543
- CPU : intel i3 5005U
- GPU : Intel HD grapics integrated
- RAM : Samsung 4GB DD3 SODIMM RAM
- HDD : Kingstone 480GB SSD
### Software (Tested)
- OS : Windows 10 22H2 / Linux mint 20.3
- Language : Python3.9
- Package distributor : Anaconda
- IDE/interface : Jupyter Notebook
### Result
- CSV dataset Generate time : 292 Seconds
- Training time : 83.47s
- Accuracy : 95.25%
- Detectction time for One image : 62ms
Given hand Image
Detected Landmark
Detected hand gesture
-- By Tirtharaj Sinha