Handwritten Digit Recognition Using ML

Abstract

Handwritten digit recognition is a fundamental problem in the field of pattern recognition and machine learning, with wide-ranging applications in document processing, postal automation, banking systems, and optical character recognition. This study presents the development of a machine learning–based model for accurate recognition of handwritten digits. The proposed system involves preprocessing techniques such as normalization, noise removal, and feature extraction to enhance the quality of input images. Various machine learning algorithms are trained and evaluated on a standard handwritten digit dataset to classify digits from 0 to 9. Model performance is assessed using accuracy, precision, recall, and confusion matrix analysis. The results demonstrate that the implemented machine learning approach achieves high recognition accuracy and robust performance across different handwriting styles. This work highlights the effectiveness of machine learning techniques in automating handwritten digit recognition tasks and provides a foundation for further improvements using advanced deep learning models.