Handwritten Digit Recognition with Convolutional Neural Networks

🧐 What Problem Does This Project Solve?

Digit recognition is a fundamental challenge in computer vision with applications in postal automation, banking, and more. This project aims to accurately recognize handwritten digits from images, bridging the gap between human handwriting and machine-readable text.

🛠️ How Was the Problem Solved?

The solution employed a Convolutional Neural Network (CNN) to classify digits from the popular MNIST dataset. Key steps included:

• Data Preprocessing: Techniques like normalization and data augmentation were applied to enhance the robustness and accuracy of the model.
• Model Optimization: Hyperparameters such as learning rate, filter count, and kernel size were fine-tuned to maximize performance.
• Evaluation: The model’s performance was assessed using metrics like accuracy and a confusion matrix, ensuring a comprehensive understanding of its strengths and weaknesses.

✅ How Close Did This Project Get to Solving the Problem?

The CNN model achieved a high accuracy of over 89% on the test dataset, demonstrating its effectiveness in recognizing handwritten digits. The results underline the potential of CNNs for practical applications in real-world handwriting recognition systems.