Machine Learning Project: EMNIST Character Recognition

Overview

This project focuses on recognizing alphanumeric characters using the EMNIST dataset. The process involves data collection and preprocessing, algorithm selection and implementation, model training and evaluation, fine-tuning, real-world application demonstrations, continuous documentation, and sharing our learning journey. The final version of our implementation is encapsulated in EMNIST_CNN_FINAL_VERSION.ipynb.

Libraries

• Scikit-learn
• Numpy
• Pandas
• Matplotlib
• Tensorflow

pip install tensorflow scikit-learn numpy pandas matplotlib

Step 1: Data Collection

1. MNIST Dataset: Access the MNIST dataset via TensorFlow or PyTorch.
2. EMNIST Dataset: Secure both training and testing subsets of the EMNIST dataset for a comprehensive numerical and alphabetical character dataset.

Step 2: Data Preprocessing

1. Data Loading: Utilize Python libraries like NumPy or pandas to load the datasets.
2. Data Exploration: Analyze the datasets to understand structure, size, format, and character distribution.
3. Preprocessing Tasks: Resize images, normalize pixel values, and encode labels.
4. Data Splitting: Divide datasets into balanced training and testing subsets.

Step 3: Algorithm Selection

Evaluate various image classification algorithms, including:

• Support Vector Machines (SVM)
• Random Forest
• Convolutional Neural Networks (CNN)
• K-Nearest Neighbors (K-NN)
• Decision Trees

Step 4: Model Training and Evaluation

1. Implementation: Use machine learning libraries for algorithm implementation.
2. Training: Train each model on the training dataset.
3. Evaluation: Assess model performance using metrics like accuracy and F1-score.
4. Comparison: Compare the performance of different algorithms. Step 5: Fine-Tuning and Experimentation
5. Hyperparameter Optimization: Adjust algorithm hyperparameters for optimal performance.
6. Experimentation: Test various preprocessing techniques and data augmentation methods.
7. Documentation: Use Jupyter Notebook for comprehensive documentation of experiments.

Step 6: Real-World Demonstrations

1. Practical Applications: Develop a presentation to showcase algorithm applications.
2. Visualizations: Create demonstrations for real-world tasks like recognizing handwritten characters.

Step 7: Maintaining Documentation

1. Progress Tracking: Document challenges, solutions, and insights.
2. Version Control: Employ GitHub for collaborative code management.

Step 8: Sharing the Learning Journey

1. Public Sharing: Publish EMNIST_CNN_FINAL_VERSION.ipynb on GitHub or similar platforms.
2. Community Resources: Create articles, blog posts, or tutorials summarizing the project.