Optical Character Recognition (OCR) MasterClass in Python

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Raj Chhabria

2:02:13

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1. Introduction

1.1 Course Introduction.pptx

1. Introduction to the Course.mp4

08:32

2.1 link to download tesseract.zip

2. Install the required libraries.mp4

03:33

2. Python Pillow (PIL Fork)

1.1 opening and viewing an image.zip

1.2 testimage.zip

1. Opening and Viewing an image.mp4

02:50

2.1 getting information about image.zip

2.2 testimage.zip

2. Obtaining information about opened image.mp4

02:27

3.1 rotate and resize.zip

3.2 testimage.zip

3. Rotate and Resize.mp4

03:47

4.1 crop an image.zip

4.2 testimage.zip

4. Crop an image using pillow.mp4

02:56

5.1 add text on image.zip

5.2 testimage.zip

5. Add text on an Image using pillow.mp4

02:40

6.1 padding.zip

6.2 testimage.zip

6. Add Padding to image with pillow.mp4

03:06

7.1 blur.zip

7.2 testimage.zip

7. Blur an image using pillow.mp4

04:03

8.1 concatnate images.zip

8.2 testimage.zip

8.3 testimage2.zip

8. Concatenate images using Pillow.mp4

06:15

9. Save an Image.mp4

02:55

3. Preprocess Images for Text OCR using OpenCV

1.1 opening an image.zip

1.2 test.zip

1. Opening an Image with OpenCV.mp4

04:57

2.1 invert an image.zip

2.2 inverted_image.zip

2.3 test.zip

2. Invert an Image.mp4

04:19

3.1 binarization.zip

3.2 binarized_img.zip

3.3 butterfly.zip

3.4 gray_img.zip

3. Binarization.mp4

05:14

4.1 erosion and dilation.zip

4. Erosion and Dilation.mp4

06:42

4. Pytesseract

1.1 image to text.zip

1.2 test.zip

1. Image to Text.mp4

02:35

2.1 test2.zip

2. Getting Boxes Around Text.mp4

06:29

3.1 test2.zip

3.2 text template matching.zip

3. Text Template Matching.mp4

07:38

4.1 license plate detection.zip

4.2 test3.zip

4. License Plate Detection.mp4

14:31

5. OCR using Machine Learning

1. Introduction to OCR using Machine Learning.mp4

06:48

2.1 K Nearest Neighbors(KNN).pptx

2.2 knn practical.zip

2. KNN Machine Learning Algorithm.mp4

10:22

3.1 digits1.zip

3.2 ocr of handwritten digits .zip

3. OCR using Machine Learning Code Implementation.mp4

09:34

Description

Learn OCR in Python using OpenCV, Pytesseract, Pillow and Machine Learning

What You'll 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 is this for?

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.

More details

Description
Welcome to Course "Optical Character Recognition (OCR) MasterClass in Python"Â

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 OFÂ 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.

Who this course is for:
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.

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Python

Raj Chhabria

Instructor's Courses

My name is Raj Chhabria and I am a Computer Science Engineer with specialization in Data Science. I am an accomplished coder and programmer, and I enjoy using my skills to contribute to student community by my Udemy Courses. Here on Udemy I intend to share my knowledge in most condensed form through my courses.

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