Realtime Sign Language Detection Using LSTM Model

Abstract

1. Introduction

This section provides an overview of the Realtime Sign Language Detection Using LSTM Model project. It describes the project's purpose, which is to develop a system that can accurately detect and interpret sign language gestures in real time. It also highlights the use of LSTM (Long Short-Term Memory) models for this task and emphasizes the project's significance in improving communication accessibility for the deaf and hard of hearing community.

2. Objective

Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.

This project demonstrates how Jupyter Notebook, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Real-time sign language detection: The system can detect and interpret sign language gestures in real time, providing immediate results.
• High accuracy: The LSTM (Long Short-Term Memory) model used in the project ensures accurate recognition of a wide range of sign language gestures.
• Multi-gesture support: The system can recognize and interpret various sign language gestures, allowing for effective communication.
• Easy integration: The project provides code snippets and examples for seamless integration into other applications or projects.
• Accessibility improvement: The Realtime Sign Language Detection Using LSTM Model project contributes to enhancing communication accessibility for the deaf and hard of hearing community.
• Customization options: The system supports customization of gestures, allowing users to adapt it to their specific needs.
• Language flexibility: The model can be trained to recognize sign language gestures from different languages, making it adaptable to various communication contexts.
• User-friendly interface: The project includes a user-friendly interface that simplifies the interaction with the system, ensuring a smooth user experience.
• Open-source: The Realtime Sign Language Detection Using LSTM Model is an open-source project, encouraging contributions and fostering collaboration in the development community.

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
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AvishakeAdhikary/Realtime-Sign-Language-Detection-Using-LSTM-Model.git
cd Realtime-Sign-Language-Detection-Using-LSTM-Model

1. Clone the repository:
2. Install Dependencies:
3. Run Jupyter Notebook:

git clone https://github.com/AvhishekAdhikary/Realtime-Sign-Language-Detection-Using-LSTM-Model.git

pip install notebook

jupyter notebook

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 AvishakeAdhikary 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.