Optical Character Recognition (OCR) MasterClass in Python

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

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Optical Character Recognition (OCR) MasterClass in Python

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...

Description

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.


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.

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Tags

  • OCR (Optical Character Recognition)

Subscribers

28440

Lectures

22

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