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 16. Number Plate Recognition with Deep Convolutional Networks

This chapter introduces us to the steps needed to create an application for Automatic Number Plate Recognition (ANPR). There are different approaches and techniques based on different situations; for example, an infrared camera, fixed car position, and light conditions. We can proceed to construct an ANPR application to detect automobile license plates in a photograph taken between two and three meters from a car, in ambiguous light conditions, and with a non-parallel ground with minor perspective distortions in the automobile's plate.

The main purpose of this chapter is to introduce us to image segmentation and feature extraction, pattern recognition basics, and two important pattern recognition algorithms, the Support Vector Machine (SVM) and deep neural network (DNN), using convolutional networks. In this chapter, we will cover the following topics:

  • ANPR
  • Plate detection
  • Plate recognition