This project is developed by me - Saptrshi Ghosh with the objective of recognizing and classifying handwritten digits. It utilizes machine learning techniques and is aimed at demonstrating a practical application within the field of computer vision.
The goal of this project is to develop a machine learning model that can accurately recognize and classify handwritten digits. This challenge addresses the complexity of varying handwriting styles and the need for high precision in digit recognition.
The model leverages the MNIST dataset, a renowned large database of handwritten digits commonly used in machine learning for training and testing purposes. Key characteristics of the MNIST dataset include:
- 60,000 training images
- 10,000 testing images
- 28x28 pixel grayscale images of digits (0-9)
- Images are labeled with the corresponding digit they represent
A Convolutional Neural Network (CNN) is employed to tackle this classification problem, thanks to its effectiveness in image recognition tasks. The CNN architecture consists of:
- Convolutional layers for feature extraction
- Max pooling layers for dimensionality reduction
- A flattening layer and a dense layer for classification
Preprocessing steps involve normalizing the pixel values of images, reshaping the images for the CNN, and one-hot encoding the labels.
- The results, including accuracy and loss, vary based on the execution of the code.
- A well-tuned CNN model on the MNIST dataset is expected to achieve high accuracy, often above 95% on the test set.
For evaluating the model's performance, the following metrics are employed:
- Accuracy: The proportion of correctly classified images in the test set.
- Loss: Specifically, categorical crossentropy, providing insights into the model's predictive performance.
This project demonstrates the effective use of a Convolutional Neural Network in accurately classifying handwritten digits, showcasing the potential of deep learning in computer vision tasks.