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

Introducing optical character recognition


Identifying text in an image is a very popular application for computer vision. This process is commonly called optical character recognition, and is divided as follows:

  • Text preprocessing and segmentation: During this step, the computer must deal with image noise, and rotation (skewing), and identify what areas are candidate text.
  • Text identification: This is the process of identifying each letter in text which will be covered in the later chapters.

The preprocessing and segmentation phase can vary greatly depending on the source of the text. Let's take a look at common situations where preprocessing is done:

  • Production OCR applications with a scanner: This is a very reliable source of text. In this scenario, the background of the image is usually white and the document is almost aligned with the scanner margins. The content that's being scanned contains basically text, with almost no noise. This kind of application relies on simple preprocessing techniques...