Handwritten Digit Classification System

A complete end-to-end system for classifying handwritten digits using deep learning, featuring a web-based interface and REST API.

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Table of Contents

• Overview
• Features
• Installation
• Usage
• Project Structure

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Overview

This project implements a convolutional neural network (CNN) trained on the MNIST dataset to recognize handwritten digits (0-9). The system includes:

• A Keras/TensorFlow deep learning model
• Flask-based REST API
• Interactive web interface
• Model training/evaluation scripts
• Preprocessing pipeline

Achieves 97.5%+ accuracy on test data while maintaining real-time prediction capabilities.

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Features

Technical Features

• Model Training Pipeline
• Data Preprocessing Utilities
• REST API Endpoints
• Web Interface with Canvas Drawing
• Error Handling and Input Validation

User Features

• Real-time digit prediction
• Interactive drawing canvas
• Mobile-responsive design
• Clear/reset functionality
• Prediction confidence display
• Drag-and-drop image support

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Installation

Prerequisites

• Python 3.8+
• pip package manager

Setup

Clone repository

git clone https://github.com/zain-ul-abideen-5036/handwritten-digit-classifier.git
cd handwritten-digit-classifier

Install dependencies

pip install -r requirements.txt

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Usage

Model Training

1. Run the Jupyter notebook:

jupyter notebook notebooks/HandwrittenDigitClassification.ipnyb

2. Execute all cells to:
   • Load and visualize data.
   • Train the model.
   • Generate plots.

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Start Web Server

python app.py

Access the web interface at http://localhost:5000

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Project Structure

handwritten-digit-classifier/
├── app.py                                          - Flask application entry point
├── HandwrittenDigitClassification.ipnyb            - Model training script
├── mnist_model.h5                                  - Pretrained model weights
├── requirements.txt                                - Dependency list
├── README.md                                       - Project documentation
│
└── templates/                                      - Frontend templates
   └── index.html                                   - Main interface

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Contact

For questions or feedback, contact: abideen5036@gmail.com

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