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https://github.com/krispyarena/nepali-sign-language-detection-using-cnn
Welcome to the Nepali Sign Language Detection project. Here, Convolutional Neural Networks (CNNs) are leveraged to recognize and interpret Nepali Sign Language(NSL) gestures.
https://github.com/krispyarena/nepali-sign-language-detection-using-cnn
Last synced: about 2 months ago
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Welcome to the Nepali Sign Language Detection project. Here, Convolutional Neural Networks (CNNs) are leveraged to recognize and interpret Nepali Sign Language(NSL) gestures.
- Host: GitHub
- URL: https://github.com/krispyarena/nepali-sign-language-detection-using-cnn
- Owner: krispyarena
- Created: 2024-04-03T13:38:20.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T10:51:46.000Z (5 months ago)
- Last Synced: 2024-11-20T16:08:51.429Z (about 2 months ago)
- Language: Jupyter Notebook
- Size: 36.6 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Nepali Sign Language Detection Using CNN
This repository contains a project for detecting Nepali Sign Language using Convolutional Neural Networks (CNN). The project aims to facilitate communication for the deaf and hard-of-hearing community by translating Nepali sign language into text.
## Features
- **Sign Language Detection**: Recognizes various Nepali sign language gestures.
- **Deep Learning Model**: Utilizes CNN for accurate detection and classification.
- **User-Friendly Interface**: Easy-to-use interface for users to interact with the system.
## Getting Started
### Prerequisites
- Python 3.x
- TensorFlow
- Keras
- OpenCV
### Installation
1. Clone the repository:
```bash
git clone https://github.com/krispyarena/nepali-sign-language-detection-using-CNN.git
cd nepali-sign-language-detection-using-CNN
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
### Usage
1. Train the model:
```bash
python train_model.py
```
2. Run the application:
```bash
python app.py
```
### Data
- The dataset used for training and testing is included in the `data` folder.
- Ensure the data is preprocessed correctly before training.
## Technologies Used
- **Python**
- **TensorFlow**
- **Keras**
- **OpenCV**
## Tools Used
- **Jupyter Notebook**
- **Visual Studio Code**
- **Github**
## Extra Resources
- **Link to Presentation** : [Project Presentation](https://docs.google.com/presentation/d/1SdRnR4mG1nWYIAjh4b76P1ztI_z6kpSQ/edit?usp=drive_link&ouid=112179910766979724441&rtpof=true&sd=true)
- **Link to Report** : [Project Report](https://drive.google.com/file/d/1GVLaBn8pk4IxoI7EX325goEOsANHDjQI/view?usp=sharing)
## Contributing
Contributions are welcome! Please fork the repository and submit pull requests for any enhancements or bug fixes.
## License
This project is licensed under the MIT License.