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Mastering OCR using Deep Learning and OpenCV-Python

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Mastering OCR using Deep Learning and OpenCV-Python, available at $59.99, has an average rating of 3.75, with 27 lectures, 2 quizzes, based on 224 reviews, and has 1054 subscribers.

You will learn about What is Optical Character Recognition (OCR)? A general OCR pipeline used by most industries. Different Image Pre-processing techniques used in OCR pipeline. Different Text Detection techniques used in OCR pipeline such as EAST and CTPN. Different Text Recognition techniques used in OCR pipeline such as CRNN (CNN+RNN+CTC) Implementing OCR on real-life examples This course is ideal for individuals who are People who have no experience with OCR and wants to learn about it. or People who have little experience of OCR and wants to master it. or People who want to make their career in OCR field. or People who want to apply OCR in their real life to automate day-to-day work. It is particularly useful for People who have no experience with OCR and wants to learn about it. or People who have little experience of OCR and wants to master it. or People who want to make their career in OCR field. or People who want to apply OCR in their real life to automate day-to-day work.

Enroll now: Mastering OCR using Deep Learning and OpenCV-Python

Summary

Title: Mastering OCR using Deep Learning and OpenCV-Python

Price: $59.99

Average Rating: 3.75

Number of Lectures: 27

Number of Quizzes: 2

Number of Published Lectures: 27

Number of Published Quizzes: 2

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

  • What is Optical Character Recognition (OCR)?
  • A general OCR pipeline used by most industries.
  • Different Image Pre-processing techniques used in OCR pipeline.
  • Different Text Detection techniques used in OCR pipeline such as EAST and CTPN.
  • Different Text Recognition techniques used in OCR pipeline such as CRNN (CNN+RNN+CTC)
  • Implementing OCR on real-life examples

Who Should Attend

  • People who have no experience with OCR and wants to learn about it.
  • People who have little experience of OCR and wants to master it.
  • People who want to make their career in OCR field.
  • People who want to apply OCR in their real life to automate day-to-day work.

Target Audiences

  • People who have no experience with OCR and wants to learn about it.
  • People who have little experience of OCR and wants to master it.
  • People who want to make their career in OCR field.
  • People who want to apply OCR in their real life to automate day-to-day work.

Hi There!

Welcome to the course ‘Mastering OCR using Deep Learning and OpenCV-Python’. This is the first course of my OCR series.

In this course we will start from the very basics. We will first discuss what is Optical Character Recognition and why you should invest your time in learning this.

Then we will move to the general pipeline used by most of the OCR systems available.

After this we will start learning each pipeline component in detail. We will start by learning some image pre-processing techniques commonly used in OCR systems.

Then we will learn some deep learning based text detection algorithms such as EAST and CTPN. We will also implement the EAST algorithm using OpenCV-Python.

Next we will learn the crux of the CTC which is widely used in developing text recognition systems. We will implement very famous text recognition algorithm that is CRNN.

Finally we will learn the last component of the OCR pipeline that is restructuring. In this we will discuss why is restructuring important for any OCR systems.

We will also discuss an open source end-to-end OCR engine which is pytesseract.

Finally we will run the complete OCR pipeline to extract the data from identification document using pytesseract.

So that’s all for this course, see you soon in the class room. Happy learning and have a great time.

Stay safe, stay healthy.

Course Curriculum

Chapter 1: Introduction to Optical Character Recognition

Lecture 1: Introduction

Chapter 2: Optical Character Recognition Pipeline

Lecture 1: OCR Pipeline

Chapter 3: OCR Pipeline -1 : Image pre-processing

Lecture 1: Introduction to Image pre-processing

Lecture 2: Image Normalization

Lecture 3: Grayscale Conversion

Lecture 4: Image Resizing

Lecture 5: Noise Removal

Lecture 6: Skew Correction

Chapter 4: OCR Pipeline -2 : Text Detection

Lecture 1: Introduction to Text Detection

Lecture 2: EAST : Efficient and Accurate Scene Text detector

Lecture 3: Implementation of EAST algorithm using OpenCV-Python

Lecture 4: CTPN : Connectionist Text Proposal Network

Lecture 5: Text Detection Datasets

Lecture 6: Additional Resources for Text Detection Datasets

Chapter 5: OCR Pipeline -3 : Text Recognition

Lecture 1: Introduction to Text Recognition

Lecture 2: CRNN model and Intuition behind CTC

Lecture 3: Connectionist Temporal Classification (CTC)

Lecture 4: Implementation of CRNN model using OpenCV-Python

Lecture 5: Text Recognition Datasets

Chapter 6: OCR Pipeline -4 : Restructuring

Lecture 1: Restructuring

Chapter 7: Implementation of end-to-end OCR pipeline using Pytesseract

Lecture 1: A Complete Guide to Pytesseract

Lecture 2: ID data Extraction

Lecture 3: License Number Plate Recognition

Chapter 8: Building an end-to-end OCR system

Lecture 1: Combining OpenCV EAST and CRNN

Lecture 2: Combining OpenCV EAST and Pytesseract

Lecture 3: Live Video OCR: Performing OCR in live video

Chapter 9: Congratulations and What's Next?

Lecture 1: Congratulations

Instructors

Rating Distribution

  • 1 stars: 10 votes
  • 2 stars: 9 votes
  • 3 stars: 27 votes
  • 4 stars: 68 votes
  • 5 stars: 110 votes

Frequently Asked Questions


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


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