Digit Recognition & Transfer Learning with CNNs

Mon, 01 Apr 2024 00:00:00 +0000

This project focused on designing, training, and analyzing convolutional neural networks (CNNs) for digit recognition and then applying transfer learning to recognize Greek characters using PyTorch.

CNN for Digit Recognition (MNIST)

We first built and trained a simple CNN model using the MNIST dataset:

Two convolutional layers (5x5 filters)
Max pooling (2x2) and dropout (0.5)
Fully connected layer (50 nodes) + final FC with log-softmax (10 classes)
Achieved ~98% test accuracy

Figure: Example of first 6 MNIST test samples.

Network Architecture

Figure: End-to-end architecture used for digit recognition.

Training Performance

Loss decreased steadily over batches/epochs.
Test loss visualized periodically across training examples.

Figure: Negative log-likelihood training and test loss.

Filter Visualization

First conv layer has 10 learned filters.
Visualized using matplotlib to show edge/orientation detection.

Figure: Learned 5x5 filters from first conv layer.

Transfer Learning to Greek Letters

Fine-tuned MNIST CNN to classify Greek characters: α, β, γ.
Froze earlier layers, retrained final classifier head.
Achieved 96–98% accuracy on unseen test samples.

Figure: Correct classification of Greek letters using transfer learning.

Transfer Learning Training Error

Convergence observed over 100+ epochs.

Figure: Error vs epoch for Greek classification.

Live Webcam Digit Recognition

Built a webcam-based interface using OpenCV
Live input frames classified in real time using the trained CNN

Figure: Live Webcam Digit Recognition

Sometimes predictions were inaccurate due to motion blur or poor lighting, but most digits were correctly classified in real-time.

Key Takeaways

5x5 and 7x7 filters outperform 3x3 in MNIST CNNs
Dropout around 0.1–0.3 provided best generalization
Smaller batch sizes (32) resulted in better accuracy
Greek letters transfer learning worked well with small data (~27 samples)
Real-time system demonstrates CNNs are efficient and deployable

Tools

Python, PyTorch, OpenCV
Jupyter Notebooks
Matplotlib for visualization

Resources

📄 Project Report PDF
🔗 GitHub Repository

This project deepened our understanding of CNN design, filter learning, transfer learning, and real-time deployment — showing the full lifecycle from dataset to deployment.

MNIST | Saikiran Juttu | Robotics Portfolio