Handwritten Text Recognition Using Machine Learning and Deep Learning

Abstract

A new area of computer vision is character recognition. A common research topic is the growing use of digital and modern technologies in practically all industries and daily activities to store, transmit, and recognise handwritten characters for usage in digital formats. Any style of handwriting can be recognised by people. The handwritten transcription cannot be recognised by the machine. We require the computer to recognise the handwritten text because of this. A computer system may recognise and digitize handwritten input from sources including pictures, handwritten documents, and other sources of text by using handwritten character recognition. The development is based on a machine learning and artificial intelligence subfield known as deep learning. There are many different approaches and strategies used to construct handwritten character recognition systems. Yet, only few of them concentrate on neural networks. Compared to earlier methods, the use of neural networks for handwritten character recognition is more efficient. The Handwritten Character Recognition System is described in this system, along with its architecture, design, and testing procedures. The objective is to show how well neural networks recognise characters in handwritten text. In order to read handwritten notes from students and instructors, this system will report on the development of a handwritten character recognition system. This system turns a handwritten transcription's image into a digital text.