Abstract
American Sign Language Detection is an open-source AI & Machine Learning project. American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language. It is built using Jupyter Notebook, Keras, Java, Computer Vision. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
American Sign Language Detection is a deep learning end to end project where we can detect American sign Language. It handles upto 29 classes. Used MobileNetV2 to train the images. It is deployed in smartphone using TF-Lite.
2. Objective
American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language.
This project demonstrates how Jupyter Notebook, Keras, Java can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- JDK 11 or later
- Maven / Gradle
- IntelliJ IDEA, Eclipse or Android Studio
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/sayannath/American-Sign-Language-Detection.git
cd American-Sign-Language-DetectionFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by sayannath and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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