Machine Learning Algorithm to Detect Hand Written Character Recognition

Abstract

Handwritten Character Recognition (HCR) is a widely researched field that aims to develop algorithms capable of identifying handwritten text. Accurate HCR is important for various applications, including document digitization, signature verification, and postal automation. Despite significant progress, current HCR systems still face several challenges, such as variability in writing styles, noise, and the presence of cursive handwriting. To overcome these challenges, machine learning algorithms have been developed to improve the recognition accuracy of handwritten text. In this paper, we will explore the different types of machine learning algorithms used for HCR and evaluate their performance. We will also discuss the preprocessing techniques used to enhance the accuracy of recognition and the challenges in implementing them. Additionally, we will examine the evaluation metrics used to measure the accuracy of recognition and the factors that affect the performance of the algorithm, and how they can be optimized. This research can contribute to the development of more accurate and efficient handwritten character recognition systems, which can have significant applications in various fields.