Handwriting Recognition Implementation: A Machine Learning Approach

Abstract

Handwritten text recognition, also referred to as handwritten character recognition, is a field of study that combines model recognition, computer vision, and artificial intelligence. In order to translate handwritten letters into relevant text and computer commands in real time, handwriting recognition systems use pattern matching. The properties of photographs and touch-screen devices can be acquired, detected, and converted into a machine-readable form by an algorithm that recognizes handwriting. An ensemble of bagged classification trees is one way to accomplish this. A bagged classification tree is an ensemble learning technique that helps to increase the efficiency and accuracy of machine learning algorithms by lowering the variance of a prediction model and addressing bias-variance trade-offs. The standard Kaggle digits dataset from (0-9) was utilised in this study to identify handwritten digits using a bagged classification method. And with an accuracy level of 0.8371, we finally came to a conclusion about the importance of the bagged classification strategy.