Project Title: American Sign Language (ASL) Image Classification

Deep learning: Image Classification using a Convolutional Neural Network

Overview

American Sign Language (ASL) serves as a primary means of communication for many deaf individuals. This project delves into the development of a convolutional neural network (CNN) designed to classify images of ASL letters. The primary goal is to construct a model capable of recognizing individual letters, laying the groundwork for the creation of a sign language translation system.

Project Structure

1. Introduction to ASL

• Brief overview of American Sign Language and its significance.
• Discussion on the relevance of computer vision systems in translating sign language to spoken language.

2. Data Loading and Preprocessing

• Utilization of a pre-shuffled dataset containing images of ASL letters.
• Loading, examining, and preprocessing the data for model training.

3. Visualizing the Training Data

• Displaying a selection of training images along with their corresponding labels (letters).

4. Dataset Examination

• Analyzing the distribution of letters in the training and test datasets to ensure balance.

5. One-Hot Encoding

• Transforming categorical labels into one-hot encoded vectors for model training.

6. Model Definition

• Building a CNN to classify ASL images with convolutional and pooling layers.
• Model summary to provide an overview of the network architecture.

7. Model Compilation

• Compiling the model with an appropriate loss function, optimizer, and evaluation metric.

8. Model Training

• Fitting the model to the training data and evaluating its performance on the validation set.

9. Model Testing

• Evaluating the trained model on the test dataset to assess its accuracy.

10. Visualizing Misclassifications

• Identifying and displaying images that were misclassified by the model for further analysis.

How to Use

1. Ensure you have the necessary dependencies installed (e.g., TensorFlow, NumPy, Matplotlib).
2. Execute the provided code cells in a Jupyter notebook or an equivalent environment.
3. Follow the step-by-step instructions for data loading, preprocessing, model training, and evaluation.
4. Examine the model's performance on the test set and visualize misclassifications.

Conclusion

The CNN model for ASL image classification gives an accuracy of 93% on the test set. Further enhancements and refinements can be explored to improve the model's accuracy and robustness using Dropuouts or Batch Normalization including considering more epochs during the training process.