Handwritten Digit Recognition using CNN (Convolutional Neural Network

Description:

Handwritten digit recognition involves using machine learning models, specifically CNNs, to classify images of handwritten digits (0-9). This task is often solved using datasets like MNIST.

Features:

1. Recognize handwritten digits (0-9).
2. Train and test on the MNIST dataset.
3. Use CNN to extract features and improve accuracy.

Requirements:

• Programming Language: Python
• Libraries/Tools:
  • TensorFlow or Keras for building and training the CNN model.
  • NumPy, Matplotlib for data manipulation and visualization.
  • MNIST dataset (can be accessed via Keras).

Instructions:

1. Load the MNIST dataset.
2. Preprocess the data (normalize images, flatten images if necessary).
3. Build a CNN model using layers like Conv2D, MaxPooling2D, Flatten, and Dense.
4. Train the model on the training data and evaluate it on test data.