Learn OCR in Python using OpenCV, Pytesseract, Pillow, and Machine Learning
This course includes:
- 2 hours of on-demand video
- 40 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
New
4.8
(2 ratings)
Created by Raj Chhabria
What you’ll learn
- Learn about Pillow Library in Python which is used for working with image data and performing 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…
Course content
5 sections • 22 lectures • 2h 2m total lengthCollapse all sections
Introduction2 lectures • 12min
- Introduction to the CoursePreview08:32
- Install the required libraries03:33
Python Pillow (PIL Fork)9 lectures • 31min
- Opening and Viewing an imagePreview02:50
- Obtaining information about opened image02:27
- Rotate and Resize03:47
- Crop an image using pillow02:56
- Add text on an Image using pillow02:40
- Add Padding to image with pillow03:06
- Blur an image using pillow04:03
- Concatenate images using Pillow06:15
- Save an Image02:55
Preprocess Images for Text OCR using OpenCV4 lectures • 21min
- Opening an Image with OpenCV04:57
- Invert an Image04:19
- Binarization05:14
- Erosion and Dilation06:42
Pytesseract4 lectures • 31min
- Image to Text02:35
- Getting Boxes Around Text06:29
- Text Template Matching07:38
- License Plate Detection14:31
OCR using Machine Learning3 lectures • 27min
- Introduction to OCR using Machine Learning06:48
- KNN Machine Learning Algorithm10:22
- OCR using Machine Learning Code Implementation09:34
Requirements
- Basic understanding of Python Programming Language.
Description
Welcome to the 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 a 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 backgrounds want to add a new skill of OCR to their resume.
- Anyone who wants to learn about OCR.
