End To End Sign Language Detection Project

Abstract

1. Introduction

Sign Language Detection is a project aimed at recognizing and interpreting hand gestures from sign language. This end-to-end solution employs the YOLOv5 object detection model to identify sign language phrases such as "Hello," "I love you," "Yes," "No," and "Please." The model can help bridge communication gaps and facilitate better understanding between individuals who use sign language and those who don't.

2. Objective

This end-to-end solution employs the YOLOv5 object detection model to identify sign language phrases such as "Hello," "I love you," "Yes," "No," and "Please."

This project demonstrates how Jupyter Notebook 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
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Pratik94229/End-to-End-Sign-Language-Detection-Project.git
cd End-to-End-Sign-Language-Detection-Project

1. Clone the repository to your local machine:
2. Created virtual enviorment using cmd in the cloned repository:
3. Install the required dependencies by running:
4. Run the notebook to train YOLOv5 model.
5. Run the application using the command:
6. Access the application: Open your web browser and go to http://localhost:8080 to access the text summarization service.
7. Select the image for which prediction is to be made.
8. The script will output the image or video with bounding boxes and class labels of detected sign language phrases.

git clone https://github.com/Pratik94229/End-to-end-Sign-Language-Detection.git

conda create -p venv python==3.8
   conda activate venv/

pip install -r requirements.txt

python app.py

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 Pratik94229 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.