MNIST Handwritten Digits Recognition - Image Classification

Dataset Information

This dataset allows you to study, analyze and recognize elements in the images. That’s exactly how your camera detects your face, using image recognition! It’s a digit recognition problem. This data set has 49,000 images of 28 X 28 size, totalling 49 MB.

Download link: https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-digits/

Libraries

The following libraries are used for this project:

• pandas: Data manipulation and analysis.
• matplotlib: Plotting and visualization.
• keras: High-level neural networks API.
• tensorflow: Backend engine for deep learning.
• scikit-learn: Machine learning library.

Model Used

A basic Convolutional Neural Network (CNN) is used to recognize the digits with high accuracy. CNNs are particularly effective for image classification tasks due to their ability to capture spatial hierarchies in images.

Neural Network Architecture:

• Input layer: 28x28 grayscale images
• Convolutional layers: Feature extraction
• Pooling layers: Downsampling
• Fully connected layers: Classification

Accuracy Achieved:

• 98.38%