Optical Character Recognition (OCR) MasterClass in Python

Optical Character Recognition (OCR) MasterClass in Python, available at $54.99, has an average rating of 4, with 22 lectures, based on 183 reviews, and has 28451 subscribers.

You will learn about Learn about Pillow Library in Python which is used for working with image data and perform various image manipulation steps. OpenCV for image preprocessing in Python. Learn about Pytesseract which is an Optical Character Recognition (OCR) tool for python. It will read and recognize the text in images, license plates, etc. You will learn to use Machine Learning for different OCR use cases and build ML models that perform OCR with over 90% accuracy. Build different OCR projects like License Plate Detection, Reading text from images etc… This course is ideal for individuals who are Python developers who are curious about Optical Character Recognition (OCR). or People from Data Science and Machine Learning background who want add a new skill of OCR in their resume. or Anyone who wants to learn about OCR. It is particularly useful for Python developers who are curious about Optical Character Recognition (OCR). or People from Data Science and Machine Learning background who want add a new skill of OCR in their resume. or Anyone who wants to learn about OCR.

Enroll now: Optical Character Recognition (OCR) MasterClass in Python

Summary

Title: Optical Character Recognition (OCR) MasterClass in Python

Price: $54.99

Average Rating: 4

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Learn about Pillow Library in Python which is used for working with image data and perform various image manipulation steps.
OpenCV for image preprocessing in Python.
Learn about Pytesseract which is an Optical Character Recognition (OCR) tool for python. It will read and recognize the text in images, license plates, etc.
You will learn to use Machine Learning for different OCR use cases and build ML models that perform OCR with over 90% accuracy.
Build different OCR projects like License Plate Detection, Reading text from images etc…

Who Should Attend

Python developers who are curious about Optical Character Recognition (OCR).
People from Data Science and Machine Learning background who want add a new skill of OCR in their resume.
Anyone who wants to learn about OCR.

Target Audiences

Python developers who are curious about Optical Character Recognition (OCR).
People from Data Science and Machine Learning background who want add a new skill of OCR in their resume.
Anyone who wants to learn about OCR.

Welcome to Course “Optical Character Recognition (OCR) MasterClass in Python”

Optical character recognition (OCR) technology is a business solution for automating data extraction from printed or written text from a scanned document or image file and then converting the text into a machine-readable form to be used for data processing like editing or searching.

BENEFITS OF OCR:

Reduce costs
Accelerate workflows
Automate document routing and content processing
Centralize and secure data (no fires, break-ins or documents lost in the back vaults)
Improve service by ensuring employees have the most up-to-date and accurate information

Some Key Learning Outcomes of this course are:

Recognition of text from images using OpenCV and Pytesseract.
Learn to work with Image data and manipulate it using Pillow Library in Python.
Build Projects like License Plate Detection, Extracting Dates and other important information from images using the concepts discussed in this course.
Learn how Machine Learning can be useful in certain OCR problems.
This course covers basic fundamentals of Machine Learning required for getting accurate OCR results.
Build Machine Learning models with text recognition accuracy of above 90%.
You will learn about different image preprocessing techniques such as grayscaling, binarization, erosion, dilation etc… which will help to improve the image quality for better OCR results.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the Course

Lecture 2: Install the required libraries

Chapter 2: Python Pillow (PIL Fork)

Lecture 1: Opening and Viewing an image

Lecture 2: Obtaining information about opened image

Lecture 3: Rotate and Resize

Lecture 4: Crop an image using pillow

Lecture 5: Add text on an Image using pillow

Lecture 6: Add Padding to image with pillow

Lecture 7: Blur an image using pillow

Lecture 8: Concatenate images using Pillow

Lecture 9: Save an Image

Chapter 3: Preprocess Images for Text OCR using OpenCV

Lecture 1: Opening an Image with OpenCV

Lecture 2: Invert an Image

Lecture 3: Binarization

Lecture 4: Erosion and Dilation

Chapter 4: Pytesseract

Lecture 1: Image to Text

Lecture 2: Getting Boxes Around Text

Lecture 3: Text Template Matching

Lecture 4: License Plate Detection

Chapter 5: OCR using Machine Learning

Lecture 1: Introduction to OCR using Machine Learning

Lecture 2: KNN Machine Learning Algorithm

Lecture 3: OCR using Machine Learning Code Implementation

Instructors

Raj Chhabria
Computer Science Engineer with Specialization in DataScience

Rating Distribution

1 stars: 11 votes
2 stars: 12 votes
3 stars: 36 votes
4 stars: 55 votes
5 stars: 69 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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