Handwritten Character Recognition

Abstract

In the past few decades, Deep Learning has proved to be a very powerful tool because of its ability to handle large amounts of data. The interest to use hidden layers has surpassed traditional techniques, especially in pattern recognition. One of the most popular deep neural networks is Convolutional Neural Networks. Since the 1950s, the early days of AI, researchers have struggled to make a system that can understand visual data. In the following years, this field came to be known as Computer Vision. In 2012, computer vision took a quantum leap when a group of researchers from the University of Toronto developed an AI model that surpassed the best image recognition algorithms and that too by a large margin. Handwritten character recognition is a field of computer science that focuses on developing computer systems that can recognize and comprehend human handwriting. Significant development has been made in this area over the years, resulting in applications in document processing, postal services, banking, and other fields. Advanced algorithms and machine learning approaches are used in this technology to study and recognize individual characters or even full sentences. Even while contemporary systems have reached outstanding accuracy rates, there are still limitations and hurdles to overcome. Handwriting styles, sizes, and forms vary, making it challenging to construct universal identification systems. The availability of diverse and sufficient training data remains an issue, and noisy or poor-quality input can reduce recognition accuracy.