Overview:

The Handwritten Digit Recognition project is a Machine Learning and Deep Learning-based system that can accurately identify digits (0–9) from handwritten images.
It uses Convolutional Neural Networks (CNNs) — a class of deep neural networks that excels in image recognition tasks — to classify handwritten numbers, such as those from the MNIST dataset.

This project demonstrates how machines can learn visual patterns and recognize handwriting just like humans.
It has real-world applications in banking (cheque processing), postal automation, form scanning, and digit-based data entry systems.

Objectives:

• To develop an intelligent system capable of recognizing handwritten digits from images.

• To train a CNN model that accurately classifies digits from 0 to 9.

• To demonstrate practical use of Deep Learning for image classification tasks.

• To implement a user-friendly interface that allows users to draw or upload digits for recognition.

Key Features:

1. AI-Powered Digit Recognition: Identifies digits from handwritten images with high accuracy.

2. Drawing Interface: Allows users to draw digits directly on a canvas and get instant predictions.

3. Image Upload Option: Supports image file input for recognition.

4. Confidence Score: Displays the model’s confidence for each predicted digit.

5. CNN Model: Built using TensorFlow/Keras for deep learning-based image processing.

6. Training on MNIST Dataset: Uses 60,000 images for training and 10,000 for testing.

7. Accuracy Visualization: Shows training and validation accuracy graphs.

8. Lightweight Interface: Web-based or mobile-based front end for easy testing.

9. Real-Time Inference: Provides instant prediction after digit input.

10. Secure and Offline Capable: Can run locally without internet dependency.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript (Canvas for drawing)

• Backend: Python (Flask / Django) / Node.js / PHP

• Machine Learning / Deep Learning:

  • Libraries: TensorFlow, Keras, NumPy, OpenCV, Matplotlib

  • Algorithms: Convolutional Neural Network (CNN), Softmax Classifier

• Dataset: MNIST Handwritten Digit Dataset (60,000 training + 10,000 testing samples)

• Database (optional): MySQL / Firebase (for storing user history)

Workflow:

1. Data Preprocessing:

   • Load the MNIST dataset.

   • Normalize pixel values (0–255 → 0–1).

   • Reshape images to 28x28 grayscale format.

2. Model Training:

   • Build and train a CNN model with convolution, pooling, and fully connected layers.

   • Use ReLU and Softmax activations for classification.

3. Prediction Phase:

   • User uploads or draws a digit.

   • The trained model processes the image and outputs the recognized digit along with probability scores.

4. Result Display:

   • The recognized digit and accuracy percentage are displayed to the user.