title: Test1
emoji: 🚀
colorFrom: purple
colorTo: indigo
sdk: docker
pinned: false
license: apache-2.0
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Sign Language Image Recognition System + Web App
An image recognition system developed with python and ML libraries to detect sign language gestures. It is connected along with a web application using flask and it was developed for an academic project.
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Table of Contents
About The Project
[![Product Name Screen Shot][product-screenshot]]
Built With
- [Python]
- [OpenCV]
- [TensorFlow]
- [Numpy]
- [Flask]
- [HTML]
- [CSS]
- [JavaScript]
Getting Started
To get a local copy up and running follow these simple example steps.
Prerequisites
This project is built using Python. You may need to have installed python and pip to install required packages.
Installation
python3 train.py
--bottleneck_dir=logs/bottlenecks
--how_many_training_steps=2000
--model_dir=inception
--summaries_dir=logs/training_summaries/basic
--output_graph=logs/trained_graph.pb
--output_labels=logs/trained_labels.txt
--image_dir=./dataset
5. Run the web application
python3 classify_webcam.py
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<!-- USAGE EXAMPLES -->
## Usage
This project can be used to recognize sign language gestures.
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<!-- CONTRIBUTING -->
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
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<!-- CONTRIBUTORS -->
## Contributors