Detection App

Sign Language Detection Web Application

AI & Machine LearningTypeScriptMIT

Abstract

Detection App is an open-source AI & Machine Learning project. Sign Language Detection Web Application. This project contains the demo application formulated in Real-Time Sign Language Detection using Human Pose Estimation published in SLRTP 2020 and presented in the ECCV 2020 demo track. It is built using TypeScript. 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

This project contains the demo application formulated in Real-Time Sign Language Detection using Human Pose Estimation published in SLRTP 2020 and presented in the ECCV 2020 demo track.

We use the tf.js models open-sourced by Google research.

This demo app is available to try at sign-language-detector.web.app

2. Objective

Sign Language Detection Web Application

This project demonstrates how TypeScript can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

TypeScript

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Node.js (LTS) and npm / yarn / pnpm
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sign-language-processing/detection-app.git
cd detection-app

Full 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.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by sign-language-processing 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.

View Source on GitHubDownload ZIP

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