Book Image

OpenCV By Example

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

OpenCV By Example

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

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
About the Authors
About the Reviewers

Using the text API

Enough of theory. It's time to see how the text module works in practice. Let's study how to use it to perform text detection, extraction, and identification.

Text detection

Let's start with creating a simple program to perform text segmentation using ERFilters. In this program, we will use the trained classifiers from text API samples. You can download them from the OpenCV repository, but they are also available in the book's companion code.

First, we start with including all the necessary libs and using:

#include  "opencv2/highgui.hpp"
#include  "opencv2/imgproc.hpp"
#include  "opencv2/text.hpp"

#include  <vector>
#include  <iostream>

using namespace std;
using namespace cv;
using namespace cv::text;

Recall from our previous section that the ERFilter works separately in each image channel. So, we must provide a way to separate each desired channel in a different single cv::Mat channel. This is done by the separateChannels function:

vector<Mat> separateChannels...