Sign Language Detection And Conversion To Text Using CNN And Open CV

Abstract

1. Introduction

The project focuses on translating American Sign Language into text using CNN model and OpenCV library of python. The training and testing of the model will done by classical convolutional neural network and then for real time application OpenCV will be used to detect the hand gesture and then the final prediction will be made by the CNN model.

The data set is a collection of images of alphabets from the American Sign Language, separated in 36 folders which represent the various classes. The training data set contains 2515 images which are 400x400 pixels. There are 36 classes, of which 26 are for the letters A-Z and 10 are numbers 0-9.The test data set contains a mere 36 images, to encourage the use of real world test images. The Data is collected from kaggle. Link : https://www.kaggle.com/ayuraj/asl-dataset

2. Objective

The project focuses on translating American Sign Language into text using CNN model and OpenCV library of python. The training and testing of the model will done by classical convolutional neural network and then for real time application OpenCV will be used to detect the hand and then the final prediction will be made by the CNN model.

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/imdasrj98/Sign-Language-Detection-and-Conversion-to-Text-Using-CNN-and-OpenCV.git
cd Sign-Language-Detection-and-Conversion-to-Text-Using-CNN-and-OpenCV

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 imdasrj98 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.