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

ANPR algorithm


Before explaining the ANPR code, we need to define the main steps and tasks in the ANPR algorithm. ANPR is divided into two main steps, plate detection and plate recognition:

  • Plate detection has the purpose of detecting the location of the plate in the whole camera frame.
  • When a plate is detected in an image, the plate segment is passed to the second step (plate recognition), which uses an OCR algorithm to determine the alphanumeric characters on the plate.

In the following diagram, we can see the two main algorithm steps, plate detection and plate recognition. After these steps, the program paints in the camera image the plate's characters that have been detected. The algorithms can return bad results, or may not return any result:

In each step shown in the previous diagram, we will define three additional steps that are commonly used in pattern recognition algorithms. These steps are as follows:

  1. Segmentation: This step detects and removes each patch/region of interest in the...