Optical Character Recognition (OCR) Using Opencv and Python: Implementation and Performance Analysis

Optical Character Recognition (OCR) Using Opencv and Python: Implementation and Performance Analysis

A. V. Senthil Kumar, Ajay Karthick M., Ahmad Fuad Hamadah Bader, Gaganpreet Kaur, Samrat Ray, Prasanna Lakshmi G., Paresh Virparia, Bharat Bhushan Sagar, Amit Dutta, Shadi R. Masadeh, Uma N. Dulhare, Asadi Srinivasulu
Copyright: © 2024 |Pages: 17
DOI: 10.4018/979-8-3693-3354-9.ch008
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Optical character recognition (OCR) stands as a transformative technology at the intersection of computer vision and document processing. This chapter explores the advancements and challenges in OCR, focusing on methods for extracting text content from images, scanned documents, and other visual media. The review encompasses traditional techniques, such as template matching and feature-based methods, as well as state-of-the-art deep learning approaches. The evolution of OCR algorithms is discussed in the context of their applications in digitizing historical archives, automating data entry, enhancing accessibility, and facilitating language translation. Additionally, attention is given to challenges related to diverse fonts, handwriting recognition, and handling complex document layouts. The chapter concludes with an outlook on emerging trends and future directions in OCR research, emphasizing the ongoing pursuit of accuracy, robustness, and efficiency in extracting textual information from visual data.
Chapter Preview
Top

Advantages Of Ocr

The benefits of optical character recognition are numerous and have made it an essential tool for many companies and organizations. OCR programs enable the automatic recognition and conversion of scanned images, Pdf’s and other documents into machine-readable text. Not only does this save time and resources by eliminating the need for manual data entry, it also improves accuracy and reduces the likelihood of errors.

It is a technology that has been around for many years and is used in various industries. The interesting thing about this technology is that it recognizes not only the characters on the page, but also the layout of the document and its formatting. This makes reading much easier for people with visual impairments because they don't have to spend time adjusting their reading settings on their device or software. This technology can be used in different ways depending on what you want to do with the scanned document.

Example: If you want to convert your scanned documents into editable files, OCR will give you an editable file that still needs to be edited and formatted before it is ready to be published. If you want to extract data from your scanned documents, OCR components will provide you with data in a spreadsheet or other format that can be easily manipulated and analyzed by other programs.

1. Increase Productivity

OCR helps businesses increase efficiency by enabling faster data retrieval when needed. It enables companies to minimize document processing time by up to 80%. By eliminating the manual process, employees can focus on other important aspects of the business. This significantly increases the company's production output.

Key Terms in this Chapter

Data Annotation: In machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict.

Recurrent Neural Network: A deep learning model that is trained to process and convert a sequential data input into a specific sequential data output.

Convolutional Neural Network: A type of artificial neural network used primarily for image recognition and processing, due to its ability to recognize patterns in images.

OpenCV: Is a great tool for image processing and performing computer vision tasks. It is an open-source library that can be used to perform tasks like face detection, objection tracking, landmark detection, and much more.

Optical Character Recognition: It is the process that converts an image of text into a machine-readable text format.

Complete Chapter List

Search this Book:
Reset