An Improved Algorithm for Optical Character Recognition using Graphical User Interface Design

Abstract

Since the COVID-19 pandemic, numerous jobs have become necessary, including the storing and sharing of printed material across computers. One simple way to save data from printed papers to a computer system is to scan them first and then save them as images. However, it would be quite challenging to extract or query text or other information from these photo files to reuse this information. As a result, a method for automatically retrieving and storing information, particularly text, from picture files is required. Optical character recognition (OCR) is an ongoing research topic that aims to create a computer system capable of extracting and processing text from images. To accomplish successful automation, certain significant problems must be identified and addressed. The font properties of characters in paper documents, as well as image quality, are only a few of the latest problems. Characters may not be recognized correctly by the computer system because of many complexities. So, in this study, authors look into OCR in four different contexts and apply them to get our results. However, every OCR is further followed by these two steps. First, a comprehensive explanation of the challenges that may develop during the OCR phases is provided. The key phases of an OCR system are then executed, including pre-processing, segmentation, normalization, feature extraction, classification, and post-processing. It can be used with deep learning software to provide OCR data which is very useful for robotic and AI applications.