Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning

Abstract

The aim of this project is to implement a classification algorithm to recognize handwritten digits (0‐ 9). It has been shown in pattern recognition that no single classifier performs the best for all pattern classification problems consistently. Hence, the scope of the project also included the elementary study the different classifiers and combination methods, and evaluate the caveats around their performance in this particular problem of handwritten digit recognition. This report presents our implementation of the Principal Component Analysis (PCA) combined with 1‐Nearest Neighbor to recognize the numeral digits, and discusses the other different classification patterns. We were able to achieve an accuracy rate of 78.4%.