Handwritten Digit Recognitio

Overview

The Handwritten Digit Recognition System is a machine learning application that identifies and classifies handwritten digits (0-9) using a trained deep learning model. This project leverages TensorFlow/Keras and a Convolutional Neural Network (CNN) to achieve high accuracy in recognizing handwritten digits from images.

Features

• Digit Classification: Identifies digits from 0 to 9.
• High Accuracy: Utilizes a CNN model for precise predictions.
• Streamlined Model Loading: Efficiently loads the pre-trained model for real-time predictions.
• Ease of Use: Simple and intuitive Python implementation.

Requirements

• Python 3.8+
• TensorFlow 2.8+
• NumPy
• Matplotlib (optional, for visualizations)
• Streamlit (optional, for building a web interface)

Installation

1. Clone the repository:

   git clone <repository_url>
   cd handwritten_digit_recognition

2. Install dependencies:

   pip install -r requirements.txt

3. (Optional) Install Streamlit for a web interface:

   pip install streamlit

Usage

1. Run Predictions with Python Script:

   python app.py

   Replace digit_recognition_model.keras with your model file if needed.

2. Input Image for Prediction:

   • Provide an image containing a handwritten digit (28x28 grayscale).
   • The model will output the predicted digit with its confidence score.

3. Run Streamlit App (if implemented):

   streamlit run app.py

Model Details

• Architecture: Convolutional Neural Network (CNN) with multiple layers, including convolutional, pooling, and dense layers.
• Dataset: Trained on the MNIST dataset, which contains 60,000 training and 10,000 testing images of handwritten digits.
• Input Shape: 28x28 grayscale images.
• Output: A single digit (0-9).

Example

Here is an example of how the system predicts a digit:

import tensorflow as tf
import numpy as np

# Load the model
model = tf.keras.models.load_model('digit_recognition_model.keras')

# Load and preprocess an image
image = np.zeros((28, 28))  # Example input image
image = image.reshape(1, 28, 28, 1) / 255.0  # Reshape and normalize

# Make a prediction
prediction = model.predict(image)
predicted_digit = np.argmax(prediction)
print(f"Predicted Digit: {predicted_digit}")

Future Enhancements

• Add support for multi-digit recognition.
• Enhance GUI/UX for a web-based interface.
• Extend support for datasets beyond MNIST.

---

Feel free to explore, contribute, or modify this project!