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Computer Vision: Python OCR and Object Detection Quick Starter
Computer Vision: Python OCR and Object Detection Quick Starter

Computer Vision: Python OCR and Object Detection Quick Starter: Get to grips with optical character recognition, image recognition, object detection, and object recognition using Python

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Profile Icon Abhilash Nelson
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₱579.99 ₱6378.99
Video Jun 2021 4hrs 41mins 1st Edition
Video
₱579.99 ₱6378.99
Subscription
Free Trial
Arrow left icon
Profile Icon Abhilash Nelson
Arrow right icon
₱579.99 ₱6378.99
Video Jun 2021 4hrs 41mins 1st Edition
Video
₱579.99 ₱6378.99
Subscription
Free Trial
Video
₱579.99 ₱6378.99
Subscription
Free Trial

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Key benefits

  • Understand the optical character recognition (OCR) technology
  • Explore convolutional neural networks pre-trained models for image recognition
  • Use Mask R-CNN pre-trained models and MobileNet-SSD for object detection

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. All resources and code files for this course are placed here: https://github.com/PacktPublishing/Computer-Vision-Python-OCR-Object-Detection-Quick-Starter

Who is this book for?

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

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Last updated date : Jun 30, 2021
Publication date : Oct 23, 2020
Length: 4hrs 41mins
Edition : 1st
Language : English
ISBN-13 : 9781800567481
Category :
Languages :
Tools :

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Product Details

Last updated date : Jun 30, 2021
Publication date : Oct 23, 2020
Length: 4hrs 41mins
Edition : 1st
Language : English
ISBN-13 : 9781800567481
Category :
Languages :
Tools :

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Table of Contents

33 Chapters
Course Introduction and Table of Contents Chevron down icon Chevron up icon
Introduction to OCR Concepts and Libraries Chevron down icon Chevron up icon
Setting Up Environment - Anaconda Chevron down icon Chevron up icon
Python Basics (Optional) Chevron down icon Chevron up icon
Tesseract OCR Setup Chevron down icon Chevron up icon
OpenCV Setup Chevron down icon Chevron up icon
Tesseract Image OCR Implementation Chevron down icon Chevron up icon
Optional: cv2.imshow() Not Responding Issue Fix Chevron down icon Chevron up icon
Introduction to CNN - Convolutional Neural Networks - Theory Session Chevron down icon Chevron up icon
Installing Additional Dependencies for CNN Chevron down icon Chevron up icon
Introduction to VGGNet Architecture Chevron down icon Chevron up icon
Image Recognition Using Pre-Trained VGGNet16 Model Chevron down icon Chevron up icon
Image Recognition Using Pre-Trained VGGNet19 Model Chevron down icon Chevron up icon
Image Recognition Using Pre-Trained ResNet Model Chevron down icon Chevron up icon
Image Recognition Using Pre-Trained Inception Model Chevron down icon Chevron up icon
Image Recognition Using Pre-Trained Xception Model Chevron down icon Chevron up icon
Introduction to MobileNet-SSD Pre-Trained Model Chevron down icon Chevron up icon
MobileNet-SSD Object Detection Chevron down icon Chevron up icon
Mobilenet SSD Real-Time Video Chevron down icon Chevron up icon
Mobilenet SSD Pre-Saved Video Chevron down icon Chevron up icon
Mask RCNN Pre-Trained Model Introduction Chevron down icon Chevron up icon
MaskRCNN Bounding Box Implementation Chevron down icon Chevron up icon
MaskRCNN Object Mask Implementation Chevron down icon Chevron up icon
MaskRCNN Real-Time Video Chevron down icon Chevron up icon
MaskRCNN Pre-saved Video Chevron down icon Chevron up icon
YOLO Pre-Trained Model Introduction Chevron down icon Chevron up icon
YOLO Implementation Chevron down icon Chevron up icon
YOLO Real-Time Video Chevron down icon Chevron up icon
YOLO Pre-Saved Video Chevron down icon Chevron up icon
Tiny YOLO Pre-Saved Video Chevron down icon Chevron up icon
Tiny YOLO Real-Time Video Chevron down icon Chevron up icon
YOLOv4 - Step 1 - Updating OpenCV Version Chevron down icon Chevron up icon
YOLOv4 - Step 2 - Object Recognition Implementation Chevron down icon Chevron up icon
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