Optical Character Recognition (OCR) MasterClass in Python

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...

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 OFOCR:

• 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.