Building a Handwritten Digit Recognition System: A Step-by-Step Journey

BLOG Website: https://math156runpeng.blogspot.com/2024/12/building-handwritten-digit-recognition.html

Welcome to my blog! Today, I want to share an exciting project I recently completed: building a Convolutional Neural Network (CNN) to recognize handwritten digits using the MNIST dataset. This project not only deepened my understanding of machine learning and computer vision but also gave me hands-on experience in deploying an interactive web application for digit recognition.

Project Overview

The goal of this project was to create a machine learning model capable of identifying handwritten digits (0-9) with high accuracy. Using the MNIST dataset, which consists of 70,000 grayscale images of handwritten digits, we trained a CNN to classify the images. To make the project more engaging, I also developed a Streamlit-powered web application where users can upload their digit images and get instant predictions.

Step 1: Data Preparation

The MNIST dataset comes pre-labeled, with each image being 28x28 pixels. To prepare the data for training:

1. We normalized the pixel values to fall within the range [0, 1].

2. The dataset was split into training and testing subsets.

This preprocessing step ensured that the model could efficiently learn without being overwhelmed by large value ranges.

Step 2: Building the CNN Model

Convolutional Neural Networks are particularly well-suited for image classification tasks due to their ability to automatically detect and learn important features. Here’s a summary of the model:

• Input Layer: Processes 28x28 grayscale images.

• Convolutional Layers: Extract spatial features from the images.

• Pooling Layers: Reduce the spatial dimensions to minimize computation.

• Dense Layers: Perform the final classification.

After training the model on the dataset, we achieved an impressive accuracy on the test set, validating the model’s effectiveness.

Step 3: Deploying the Application

To make the project user-friendly, I developed a web application using Streamlit. The app allows users to:

• Upload images of handwritten digits.

• View real-time predictions generated by the trained model.

The interface is clean, intuitive, and demonstrates how AI can be applied to solve everyday problems.

Challenges and Lessons Learned

Throughout the project, I encountered and overcame several challenges:

1. Handling Missing Dependencies: Ensuring all libraries and tools were compatible across different systems.

2. Early Stopping: To avoid overfitting, I implemented early stopping during training. This strategy halted training when the validation accuracy stopped improving.

3. File Path Generalization: Making the project replicable for others required careful consideration of file paths and instructions in the README file.

Future Improvements

Although the project was a success, there’s always room for growth:

• Data Augmentation: Incorporating transformations like rotations and zooms to make the model more robust.

• Expanding the App’s Features: Adding real-time camera input for digit recognition could make the app even more interactive.

How You Can Try It

Interested in trying out the project? You can find the code and detailed instructions in the GitHub repository https://github.com/PwanPwannn/Math156-Machine-Learning-Project-Handwritten-Number-Recognition_Runpeng_William_Jiawei.

The following picture is an illustration of how you can use the final digit recognition app to predict your written digits uploaded via the portal.

Digit Recognition App_Testing with number'5'

Feel free to clone the repository, follow the steps in the README file, and experience the power of machine learning yourself.

Final Thoughts

This project was an incredible learning experience, bridging the gap between theoretical knowledge and practical implementation. It’s fascinating to see how machine learning models, paired with user-friendly applications, can make technology accessible to everyone.

If you’re interested in machine learning or have questions about the project, feel free to leave a comment below. Let’s continue exploring the endless possibilities of AI together!

Thanks for reading, and see you in the next post!