Computer Vision: Python OCR and Object Detection Quick Starter

Computer Vision: Python OCR and Object Detection Quick Starter

by Abhilash Nelson
Released October 2020
Publisher(s): Packt Publishing
ISBN: 9781800567481

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Video description

This course is a quick starter for anyone who wants to explore optical character recognition (OCR), image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process.Starting with an introduction to the OCR technology, you'll get your system ready for Python coding by installing Anaconda packages and the necessary libraries and dependencies.

As you advance, you'll work with convolutional neural networks (CNNs), the Keras library, and pre-trained models such as VGGNet 16 and VGGNet 19, to perform image recognition with the help of sample images. The course then focuses on object recognition and shows you how to use MobileNet-SSD and Mask R-CNN pre-trained models to detect and label objects in a real-time live video from the computer's webcam as well as in a saved video. Toward the end, you'll learn how the YOLO model and the lite version, Tiny YOLO, fasten the process of detecting an object from a single image.

By the end of the course, you'll have developed a solid understanding of OCR and the methods involved and gain the confidence to perform optical character recognition using Python with ease.

What You Will Learn

  • Install Anaconda packages, dependencies, and libraries such as Tesseract, OpenCV, pillow
  • Get to grips with optical character recognition in Python using the tesseract library
  • Perform image recognition using VGGNet 16, VGGNet 19, ResNet, Inception, and Xception pre-trained models in the Keras library
  • Explore object recognition using MobileNet SSD, Mask R-CNN, YOLO
  • Achieve a perfect blend of speed and accuracy in object detection and recognition
  • Learn about optical character recognition with tesseract library and image recognition using Keras

Audience

This course is for beginners or anyone who wants to get started with Python-based OCR, image recognition, and object recognition.

About The Author

Abhilash Nelson: Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

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Table of contents

  1. Chapter 1 : Course Introduction and Table of Contents
    1. Course Introduction and Table of Contents
  2. Chapter 2 : Introduction to OCR Concepts and Libraries
    1. Introduction to OCR Concepts and Libraries
  3. Chapter 3 : Setting Up Environment - Anaconda
    1. Setting Up environment - Anaconda
  4. Chapter 4 : Python Basics (Optional)
    1. Python Basics - Part 1 - Assignment
    2. Python Basics - Part 2 - Flow Control
    3. Python Basics - Part 3 - Data Structures
    4. Python Basics - Part 3 - Functions
  5. Chapter 5 : Tesseract OCR Setup
    1. Tesseract OCR Setup - Part 1
    2. Tesseract OCR Setup - Part 2
  6. Chapter 6 : OpenCV Setup
    1. OpenCV Setup
  7. Chapter 7 : Tesseract Image OCR Implementation
    1. Tesseract Image OCR Implementation - Part 1
    2. Tesseract Image OCR Implementation - Part 2
  8. Chapter 8 : Optional: cv2.imshow() Not Responding Issue Fix
    1. Optional: cv2.imshow() Not Responding Issue Fix
  9. Chapter 9 : Introduction to CNN - Convolutional Neural Networks - Theory Session
    1. Introduction to CNN - Convolutional Neural Networks - Theory Session
  10. Chapter 10 : Installing Additional Dependencies for CNN
    1. Installing Additional Dependencies for CNN
  11. Chapter 11 : Introduction to VGGNet Architecture
    1. Introduction to VGGNet Architecture
  12. Chapter 12 : Image Recognition Using Pre-Trained VGGNet16 Model
    1. Image Recognition Using Pre-Trained VGGNet16 Model - Part 1
    2. Image Recognition Using Pre-Trained VGGNet16 Model - Part 2
  13. Chapter 13 : Image Recognition Using Pre-Trained VGGNet19 Model
    1. Image Recognition Using Pre-Trained VGGNet19 Model
  14. Chapter 14 : Image Recognition Using Pre-Trained ResNet Model
    1. Image Recognition Using Pre-Trained ResNet Model
  15. Chapter 15 : Image Recognition Using Pre-Trained Inception Model
    1. Image Recognition Using Pre-Trained Inception Model
  16. Chapter 16 : Image Recognition Using Pre-Trained Xception Model
    1. Image Recognition Using Pre-Trained Xception Model
  17. Chapter 17 : Introduction to MobileNet-SSD Pre-Trained Model
    1. Introduction to MobileNet-SSD Pre-trained Model
  18. Chapter 18 : MobileNet-SSD Object Detection
    1. Mobilenet SSD Object Detection - Part 1
    2. Mobilenet SSD Object Detection - Part 2
  19. Chapter 19 : Mobilenet SSD Real-Time Video
    1. Mobilenet SSD Real-Time Video
  20. Chapter 20 : Mobilenet SSD Pre-Saved Video
    1. Mobilenet SSD Pre-Saved Video
  21. Chapter 21 : Mask RCNN Pre-Trained Model Introduction
    1. MaskRCNN Pre-Trained Model Introduction
  22. Chapter 22 : MaskRCNN Bounding Box Implementation
    1. MaskRCNN Bounding Box Implementation - Part 1
    2. MaskRCNN Bounding Box Implementation - Part 2
  23. Chapter 23 : MaskRCNN Object Mask Implementation
    1. MaskRCNN Object Mask Implementation - Part 1
    2. MaskRCNN Object Mask Implementation - Part 2
  24. Chapter 24 : MaskRCNN Real-Time Video
    1. MaskRCNN Real-Time Video - Part 1
    2. MaskRCNN Real-Time Video - Part 2
  25. Chapter 25 : MaskRCNN Pre-saved Video
    1. MaskRCNN Pre-saved Video
  26. Chapter 26 : YOLO Pre-Trained Model Introduction
    1. YOLO Pre-Trained Model Introduction
  27. Chapter 27 : YOLO Implementation
    1. YOLO Implementation - Part 1
    2. YOLO Implementation - Part 2
  28. Chapter 28 : YOLO Real-Time Video
    1. YOLO Real-Time Video
  29. Chapter 29 : YOLO Pre-Saved Video
    1. YOLO Pre-Saved Video
  30. Chapter 30 : Tiny YOLO Pre-Saved Video
    1. Tiny YOLO Pre-Saved Video
  31. Chapter 31 : Tiny YOLO Real-Time Video
    1. Tiny YOLO Real-Time Video
  32. Chapter 32 : YOLOv4 - Step 1 - Updating OpenCV Version
    1. YOLOv4 - Step 1 - Updating OpenCV Version
  33. Chapter 33 : YOLOv4 - Step 2 - Object Recognition Implementation
    1. YOLOv4 - Step 2 - Object Recognition Implementation

Product information

  • Title: Computer Vision: Python OCR and Object Detection Quick Starter
  • Author(s): Abhilash Nelson
  • Release date: October 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781800567481