This project focuses on the recognition of handwritten digits using machine learning techniques. The goal is to build a model that can accurately classify images of handwritten digits into their corresponding numeric values.

Project Overview

  • Objective: Develop a machine learning model to recognize handwritten digits.
  • Technologies: Python, TensorFlow, Keras
  • Dataset: MNIST dataset, which contains 60,000 training images and 10,000 testing images of handwritten digits.

Dataset

The MNIST dataset is a large database of handwritten digits commonly used for training various image processing systems. The dataset contains 28x28 pixel grayscale images of handwritten digits.

Dataset Structure

  • Training Set: 60,000 images
  • Testing Set: 10,000 images

Model Architecture

The model architecture used in this project is a simple Convolutional Neural Network (CNN). The CNN consists of the following layers:

  • Convolutional Layer: Applies filters to the input image to extract features.
  • Pooling Layer: Reduces the spatial dimensions of the feature maps.
  • Dense Layer: Fully connected layer that outputs the final predictions.

Training Process

The model is trained using the following steps:

  1. Load the MNIST dataset.
  2. Preprocess the data (normalize and reshape).
  3. Split the data into training and testing sets.
  4. Compile the model with an appropriate loss function and optimizer.
  5. Train the model using the training data.
  6. Evaluate the model using the testing data.

Results

The model achieved an accuracy of 98.5% on the testing dataset.

Further Reading

For more information on machine learning and image processing, please visit our Machine Learning and Image Processing sections.

Handwritten Digit Recognition Example