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https://github.com/shubhamcweb/asldictionarysearch
Computer vision based project on recognizing signs in American Sign Language (ASL) by pattern recognition in Python with Google Colab using Numpy, Scipy and MatPlotLib utilizing nearest neighbor classification on ASL datasets.
https://github.com/shubhamcweb/asldictionarysearch
Last synced: about 1 month ago
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Computer vision based project on recognizing signs in American Sign Language (ASL) by pattern recognition in Python with Google Colab using Numpy, Scipy and MatPlotLib utilizing nearest neighbor classification on ASL datasets.
- Host: GitHub
- URL: https://github.com/shubhamcweb/asldictionarysearch
- Owner: shubhamcweb
- License: mit
- Created: 2024-09-27T01:03:25.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-31T06:58:17.000Z (about 2 months ago)
- Last Synced: 2024-10-31T07:26:47.018Z (about 2 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 231 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ASL Search
## Description
ASL Search is a computer vision project aimed at recognizing signs in American Sign Language (ASL) using pattern recognition techniques. This project was developed and presented at the OurCS@DFW UTA Student Computing Research Festival 2023.
The system uses nearest neighbor classification on ASL datasets provided by Dr. Vassilis Athitsos' research to identify and interpret ASL signs. By leveraging Python and Google Colab and libraries like NumPy, SciPy and MatPlotLib, this project demonstrates the potential of machine learning in bridging communication gaps for the deaf and hard-of-hearing community.
## Features
- Recognizes ASL signs using computer vision techniques
- Implements nearest neighbor classification for pattern recognition
- Utilizes ASL datasets from Dr. Vassilis Athitsos' research
- Developed and run in Google Colab for easy accessibility and collaboration
## Technologies Used
- Python
- Google Colab
- NumPy
- SciPy
- Matplotlib
## Installation
As this project is developed in Google Colab, no local installation is required.
## Usage
1. Run the cells in the notebook sequentially.
2. The notebook includes functions for:
- Loading and processing ASL sign data
- Standardizing sign representations
- Implementing nearest neighbor classification
- Evaluating accuracy of the classification
3. Key functions include:
- `load_annotations()` and `load_handface()` for loading dataset information
- `standardize3()` for normalizing sign data
- `make_set3()` for creating standardized datasets
- `compute_dist3()` for computing distances between signs
- `topk_accuracy()` for evaluating the model's accuracy
4. The notebook demonstrates the improvement of the model through different iterations, with the final version using both dominant and non-dominant hand data.
5. You can modify the `fixed_size` variable to adjust the number of frames used in the standardized representation of signs.
6. The final accuracy of the model is printed at the end of the notebook.
## Contributors
- Shubham Chandravanshi
- Dr. Vassilis Athitsos
## Acknowledgments
- Dr. Vassilis Athitsos, Professor of Computer Science and Engineering at The University of Texas at Arlington, for providing the ASL datasets and research guidance.
- Email: [email protected]
- Research Interests: Computer Vision, Machine Learning, Data Mining, Gesture and Sign Language Recognition, Face Recognition
- OurCS@DFW UTA Student Computing Research Festival 2023 organizers and mentors.
## License
MIT License
Copyright (c) 2024 Shubham Chandravanshi