Handwritten Characters to Electronic Readable Text Using Deep Learning

Handwritten character recognition (HCR) is the process of locating characters in images, documents, and other sources, and converting them into formats that computers can process. A considerable obstacle still exists in the perfect detection of complexly developed combined handwritten contents. In order to improve HCR, convolutional neural networks (CNN) have recently made significant strides. They do this by learning discriminative properties from significant volumes of raw data. The goal of this project is to identify handwritten characters and provide identifiable Text using those characters. Image rectification, segmentation, and character recognition make up characters. The input characters are digitalized during image correction, making them available for additional processing. One of the burgeoning disciplines in computer vision is character recognition. Humans are the only species with the ability to recognise anything. Humans are capable of quickly identifying the hand transcribing. There are several patterns to look for in different languages. The text can be correctly identified by humans. The MNIST Dataset is used in this study. The Convolutional Neural Network and Support Vector Machine approach is employed in this case because it provides high accuracy and performs superior than most of the other algorithms.