Handwritten Digit Recognition System

Developed a handwritten digit recognition system using Machine Learning and Image Processing techniques.

The project focuses on recognizing handwritten digits by preprocessing the input images, extracting meaningful features, reducing the feature dimensions, and training a machine learning classification model.

Key techniques used in the project include image preprocessing, Wavelet Transform for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification.

The system was evaluated on handwritten digit data to measure the classification performance and recognition accuracy.