Book Image

Qt 5 and OpenCV 4 Computer Vision Projects

By : Zhuo Qingliang
4 (1)
Book Image

Qt 5 and OpenCV 4 Computer Vision Projects

4 (1)
By: Zhuo Qingliang

Overview of this book

OpenCV and Qt have proven to be a winning combination for developing cross-platform computer vision applications. By leveraging their power, you can create robust applications with both an intuitive graphical user interface (GUI) and high-performance capabilities. This book will help you learn through a variety of real-world projects on image processing, face and text recognition, object detection, and high-performance computing. You’ll be able to progressively build on your skills by working on projects of increasing complexity. You’ll begin by creating an image viewer application, building a user interface from scratch by adding menus, performing actions based on key-presses, and applying other functions. As you progress, the book will guide you through using OpenCV image processing and modification functions to edit an image with filters and transformation features. In addition to this, you’ll explore the complex motion analysis and facial landmark detection algorithms, which you can use to build security and face detection applications. Finally, you’ll learn to use pretrained deep learning models in OpenCV and GPUs to filter images quickly. By the end of this book, you will have learned how to effectively develop full-fledged computer vision applications with OpenCV and Qt.
Table of Contents (11 chapters)

Chapter 5, Optical Character Recognition

  1. How is it possible to recognize characters in non-English languages with Tesseract?

Specify the corresponding language name when initializing the TessBaseAPI instance.

  1. When we used the EAST model to detect text areas, the detected areas are actually rotated rectangles, and we simply use their bounding rectangles instead. Is this always correct? If not, how can this approach be rectified?

It is correct, but this is not the best approach. We can copy the region in the bounding boxes of the rotated rectangles to new images, and then rotate and crop them to transform the rotated rectangles into regular rectangles. After that, we will generally get better outputs by sending the resulting regular rectangles to Tesseract in order to extract the text.

  1. Try to figure out a way to allow users to adjust the selected region after dragging the mouse...