Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 10. Developing Segmentation Algorithms for Text Recognition

In the previous chapters, we learned about a wide range of image processing techniques such as thresholding, contours descriptors, and mathematical morphology. In this chapter, we will discuss common problems that you may face while dealing with scanned documents, such as identifying where the text is or adjusting its rotation. We will also learn how to combine techniques presented in the previous chapters to solve those problems. By the end of this chapter, we will have segmented regions of text that can be sent to an optical character recognition (OCR) library.

By the end of this chapter, you should be able to answer the following questions:

  • What kind of OCR applications exists?
  • What are the common problems while writing an OCR application?
  • How do I identify regions of documents?
  • How do I deal with problems like skewing and other elements in the middle of the text?
  • How do I use Tesseract OCR to identify my text?