Handwritten Digit Recognition from 0 to 9 Using KNN Algorithm

MATLAB 1.98M 295 views 0 downloads 1 credits
Tags:

Resource Overview

Implementation of KNN algorithm for 0-9 handwritten digit classification achieving over 90% accuracy, featuring separate training (7291 samples) and testing (2791 samples) datasets. The MATLAB-based solution includes comprehensive documentation with clear code structure and algorithm explanations.

Detailed Documentation

This article demonstrates the implementation of K-Nearest Neighbors (KNN) algorithm for handwritten digit recognition ranging from 0 to 9. The classification system achieves an accuracy exceeding 90% through proper feature extraction and distance-based voting mechanism. Our implementation utilizes two distinct datasets: a training set containing 7291 labeled digit samples for model building, and a test set with 2791 samples for performance validation. The MATLAB code employs efficient vectorization techniques for distance calculation between test instances and all training samples, followed by majority voting among k-nearest neighbors. Key functions include data normalization, Euclidean distance computation, and accuracy evaluation metrics. The solution is accompanied by detailed documentation explaining the algorithm workflow, parameter tuning (optimal k-value selection), and result interpretation for better understanding of the machine learning pipeline.

You May Also Like