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Computer Vision – OCR using Python

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Computer Vision – OCR using Python, available at $54.99, has an average rating of 4.7, with 90 lectures, 6 quizzes, based on 242 reviews, and has 1116 subscribers.

You will learn about A quick starter on OCR Architecture, Commercial Solutions and Use Cases in Industry Learn to implement OCR – Text Detection with OpenCV and Deep Learning Models Use Tesseract and EasyOCR to implement OCR – Text Recognition Work with OCR – Text Labelling using Spacy and Regular Expression Use OpenCV and Tesseract to apply Noise Removal Techniques including Thresholding, Rescaling, Dilation, Erosion and Deskewing Learn to develop web-based applications – Business Card Recognition and KYC Digitization for OCR using Flask Build OCR Solutions for Invoice Processing with Text Labelling and XML output & Vehicle Nameplate Recognition Executable Code of CTPN and EAST Model implementation for Text Detection and Text Recognition Learn to train Deep Learning Models of CTPN and EAST on ICDAR dataset Understand the Image Basics and apply it for Image Processing This course is ideal for individuals who are Beginners to Computer Vision or OCR Engineer or OCR Specialist or Machine Learning Professionals or Anyone looking to become more employable as a Computer Vision Expert It is particularly useful for Beginners to Computer Vision or OCR Engineer or OCR Specialist or Machine Learning Professionals or Anyone looking to become more employable as a Computer Vision Expert.

Enroll now: Computer Vision – OCR using Python

Summary

Title: Computer Vision – OCR using Python

Price: $54.99

Average Rating: 4.7

Number of Lectures: 90

Number of Quizzes: 6

Number of Published Lectures: 90

Number of Published Quizzes: 6

Number of Curriculum Items: 96

Number of Published Curriculum Objects: 96

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • A quick starter on OCR Architecture, Commercial Solutions and Use Cases in Industry
  • Learn to implement OCR – Text Detection with OpenCV and Deep Learning Models
  • Use Tesseract and EasyOCR to implement OCR – Text Recognition
  • Work with OCR – Text Labelling using Spacy and Regular Expression
  • Use OpenCV and Tesseract to apply Noise Removal Techniques including Thresholding, Rescaling, Dilation, Erosion and Deskewing
  • Learn to develop web-based applications – Business Card Recognition and KYC Digitization for OCR using Flask
  • Build OCR Solutions for Invoice Processing with Text Labelling and XML output & Vehicle Nameplate Recognition
  • Executable Code of CTPN and EAST Model implementation for Text Detection and Text Recognition
  • Learn to train Deep Learning Models of CTPN and EAST on ICDAR dataset
  • Understand the Image Basics and apply it for Image Processing

Who Should Attend

  • Beginners to Computer Vision
  • OCR Engineer
  • OCR Specialist
  • Machine Learning Professionals
  • Anyone looking to become more employable as a Computer Vision Expert

Target Audiences

  • Beginners to Computer Vision
  • OCR Engineer
  • OCR Specialist
  • Machine Learning Professionals
  • Anyone looking to become more employable as a Computer Vision Expert

** A comprehensive course for people who would like to become Computer Vision – Optical Character Recognition (OCR) Specialist. This course contains 33 downloadable Source Code Resources and Projects on Invoice Processing, KYC Digitization, Business Card Recognition and Automatic Number Plate Recognition **

Top 3 Reasons on why this course Computer Vision: OCR using Python stands-out among other courses:

· Inclusion of 5 in-demand projects of Computer Vision that have been explained through detailed code walkthrough and work seamlessly

· Dedicated In-Course Support is provided within 24 hours for any issues faced

· Comprehensive Coverage inclusive of theory and practical implementation of 2 Deep learning-based Text Detection models (CTPN and EAST)

Optical Character Recognition commonly called as OCR is the new buzzword in Artificial Intelligence Industry which is driving Digitization in the enterprises. Every enterprise wants to adopt OCR to achieve easier and quicker access to their streams of data in digital format. An OCR implementation not only speed up the workflow of Text processes across various industries but also help in providing better customer experience. In fact, as per a recent research report, OCR market which was around 7.2 billion US Dollar is expected to see a huge growth in market size and will reach 13.4 billion US dollar by 2025.

Enroll in this course to get a complete understanding of Optical Character Recognition (OCR) for Data Extraction from Images and PDF using Python. The course explains the theory of concepts followed by code demonstration to make you an expert in computer vision OCR. It provides hands-on guidance on Text Detection with OpenCV and Deep Learning Models, Text Recognition with Tesseract and OCR along with Text Labelling through Spacy and Regular Expression. It guides you to create technical solutions on most relevant OCR uses cases in the industry where we are using OCR to convert image to text.

Here are just few of the topics we will be learning:

· OCR Architecture

· Text Detection from Image

· Text Recognition from Image

· Pixels and Image Basics

· Image Properties

· Kernel and Feature Map

· Preprocessing Techniques (Binarisation, Thresholding, Rescaling)

· Noise Removal Techniques (Morphology, Dilation, Erosion, Blurring, Orientation, Deskewing, Borders, Perspective Transformation)

· Image Segmentation

· EasyOCR

· PyTesseract Operations

· Tesseract

· Named Entity Recognition

· Spacy for Named Entity Recognition

· Regular Expression for Text and Dates

· Training of CTPN and EAST Deep Learning Model on SIROE Dataset

· CTPN Model for Text Detection & Text Recognition

· EAST Model for Text Detection & Text Recognition

· Invoice Processing OCR Solution with python code

· Invoice Structured Output in XML Format Solution with python code

· Vehicle Nameplate OCR Solution with python code

· Business Card Recognition OCR Solution with python code

· KYC Digitization OCR Solution with python code

Course Curriculum

Chapter 1: Course Starter

Lecture 1: Learning Path to Become Computer Vision Expert

Lecture 2: Course Starter – How to approach the course

Lecture 3: Udemy Review

Chapter 2: OCR Starter – OCR Architecture

Lecture 1: Objectives

Lecture 2: OCR Overview

Lecture 3: OCR Architecture

Lecture 4: OCR Solutions

Lecture 5: OCR Benefits

Lecture 6: OCR Use Case Across Industry

Chapter 3: Setting up Environment – Ubuntu, Windows

Lecture 1: Objectives

Lecture 2: Tool Setup – Ubuntu

Lecture 3: Tool Setup – Windows

Lecture 4: Setup Issues Resolution

Lecture 5: Using Google Colab

Lecture 6: Using Pycharm for Coding

Lecture 7: Using Jupyter Notebook and Shortcuts

Chapter 4: Image Basics – Pixels, Kernel, Image Properties

Lecture 1: Objectives

Lecture 2: Pixels and Images

Lecture 3: Image Properties

Lecture 4: Kernel

Lecture 5: Feature Map

Chapter 5: Text Detection – Machine Learning Techniques (Noise Removal, Thresholding)

Lecture 1: Objectives

Lecture 2: Text Detection Workflow

Lecture 3: Preprocessing for Accuracy Improvement

Lecture 4: Noise Removal Techniques (Morphology, Image Blurring, Dilation, Erosion, Deskew)

Lecture 5: Implement Preprocessing Techniques (Adaptive, Otsu Binarisation, Gaussian Blur)

Lecture 6: Segmentation

Lecture 7: Implement Segmentation (Line, Word and Character Level Segmentation)

Chapter 6: Introduction to Neural Networks and Text Detection Models

Lecture 1: Objectives

Lecture 2: What is a Neuron?

Lecture 3: Neuron Architecture

Lecture 4: Artificial Neural Network

Lecture 5: Convolutional Neural Network

Lecture 6: Activation Function

Lecture 7: Deep Learning – CTPN Model

Lecture 8: Deep Learning – EAST Model

Lecture 9: Annotation for OCR

Lecture 10: Further Reading – Open Source Text Detection Tools

Chapter 7: Text Detection – Deep Learning Techniques (CTPN, EAST)

Lecture 1: Installation Guide for Running Text Detection Code on Windows (CTPN, EAST)

Lecture 2: Installation Guide for Running Text Detection Code on Ubuntu (CTPN, EAST)

Lecture 3: Code Walkthrough – CTPN Implementation for Text Detection

Lecture 4: Code Download – CTPN Implementation for Text Detection

Lecture 5: CTPN Text Detection on Google Colab

Lecture 6: CTPN Training using ICDAR SIROE Dataset

Lecture 7: Code Walkthrough – EAST Implementation for Text Detection

Lecture 8: Code Compilation Guidelines for EAST on Windows

Lecture 9: Code Download – EAST Implementation for Text Detection

Lecture 10: EAST Text Detection on Google Colab

Lecture 11: EAST Training using ICDAR SIROE Dataset

Chapter 8: Text Recognition – EasyOCR, Tesseract, PyTesseract

Lecture 1: Objectives

Lecture 2: EasyOCR

Lecture 3: EasyOCR Implementation

Lecture 4: Tesseract

Lecture 5: Tesseract PSM and OEM Mode

Lecture 6: PyTesseract Operations

Lecture 7: Tesseract Implementation

Lecture 8: Further Reading – Open Source Text Recognition Tools

Chapter 9: Text Recognition – Deep Learning Techniques (CTPN, EAST)

Lecture 1: Installation Guide for Running Text Recognition Code on Windows (CTPN, EAST)

Lecture 2: Installation Guide for Running Text Recognition Code on Ubuntu (CTPN, EAST)

Lecture 3: Code Walkthrough – CTPN Implementation for Text Recognition

Lecture 4: Code Download – CTPN Implementation for Text Recognition

Lecture 5: CTPN Text Recognition on Google Colab

Lecture 6: Code Walkthrough – EAST Implementation for Text Recognition

Lecture 7: Code Compilation Guidelines for EAST on Windows

Lecture 8: Code Download – EAST Implementation for Text Recognition

Lecture 9: EAST Text Recognition on Google Colab

Chapter 10: Natural Language Processing with RegEx, Spacy

Lecture 1: Objectives

Lecture 2: Named Entity Recognition

Lecture 3: Spacy

Lecture 4: Spacy for Named Entity Recognition and Part-Of-Speech

Lecture 5: Regular Expression for Text Labelling

Lecture 6: Regular Expression for Dates

Lecture 7: Regular Expression Implementation

Chapter 11: 5 Live Projects

Lecture 1: Project 1 – Number Plate Recognition – Project Overview

Lecture 2: Project 1 – Number Plate Recognition – Code Walkthrough

Lecture 3: Project 1 – Number Plate Recognition – Code Download Instructions

Lecture 4: Project 2 – Invoice Processing with Text Labelling – Project Overview

Lecture 5: Project 2 – Invoice Processing with Text Labelling – Code Walkthrough

Lecture 6: Project 2 – Invoice Processing with Text Labelling – Code Download Instructions

Lecture 7: Project 3 – Invoice Processing with XML Output – Project Overview

Lecture 8: Project 3 – Invoice Processing with XML Output – Code Walkthrough

Lecture 9: Project 3 – Invoice Processing with XML Output – Code Download Instructions

Lecture 10: Project 4 – Business Card Recognition – Project Overview

Instructors

Rating Distribution

  • 1 stars: 10 votes
  • 2 stars: 8 votes
  • 3 stars: 26 votes
  • 4 stars: 44 votes
  • 5 stars: 154 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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