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Optical Character Recognition (OCR) in Python

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  • 3,860 Students
  • Updated 4/2023
  • Certificate Available
4.7
(401 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
12 Hour(s) 58 Minute(s)
Language
English
Taught by
Jones Granatyr, Gabriel Alves, AI Expert Academy
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.7
(401 Ratings)
2 views

Course Overview

Optical Character Recognition (OCR) in Python

OpenCV, Tesseract, EasyOCR and EAST applied to images and videos! Create your own OCR from scratch using Deep Learning!

Within the area of Computer Vision is the sub-area of Optical Character Recognition (OCR), which aims to transform images into texts. OCR can be described as converting images containing typed, handwritten or printed text into characters that a machine can understand. It is possible to convert scanned or photographed documents into texts that can be edited in any tool, such as the Microsoft Word. A common application is automatic form reading, in which you can send a photo of your credit card or your driver's license, and the system can read all your data without the need to type them manually. A self-driving car can use OCR to read traffic signs and a parking lot can guarantee access by reading the license plate of the cars!

To take you to this area, in this course you will learn in practice how to use OCR libraries to recognize text in images and videos, all the code implemented step by step using the Python programming language! We are going to use Google Colab, so you do not have to worry about installing libraries on your machine, as everything will be developed online using Google's GPUs! You will also learn how to build your own OCR from scratch using Deep Learning and Convolutional Neural Networks! Below you can check the main topics of the course:

  • Recognition of texts in images and videos using Tesseract, EasyOCR and EAST

  • Search for specific terms in images using regular expressions

  • Techniques for improving image quality, such as: thresholding, color inversion, grayscale, resizing, noise removal, morphological operations and perspective transformation

  • EAST architecture and EasyOCR library for better performance in natural scenes

  • Training an OCR from scratch using TensorFlow and modern Deep Learning techniques, such as Convolutional Neural Networks

  • Application of natural language processing techniques in the texts extracted by OCR (word cloud and named entity recognition)

  • License plate reading

These are just some of the main topics! By the end of the course, you will know everything you need to create your own text recognition projects using OCR!

Course Content

  • 13 section(s)
  • 95 lecture(s)
  • Section 1 Introduction
  • Section 2 OCR with Tesseract
  • Section 3 Techniques for image pre-processing
  • Section 4 OCR with EAST for natural scenes
  • Section 5 Training a custom OCR
  • Section 6 Natural scenarios with EasyOCR
  • Section 7 OCR in videos
  • Section 8 Project 1: Searching for specific terms
  • Section 9 Project 2: Scanner + OCR
  • Section 10 Project 3: License plate reading
  • Section 11 Extra content 1: artificial neural networks
  • Section 12 Extra content 2: convolutional neural networks
  • Section 13 Final remarks

What You’ll Learn

  • Use Tesseract, EAST and EasyOCR tools for text recognition in images and videos
  • Understand the differences between OCR in controlled and natural environments
  • Apply image pre-processing techniques to improve image quality, such as: thresholding, inversion, resizing, morphological operations and noise reduction
  • Use EAST architecture and EasyOCR library for better performance in natural scenes
  • Train an OCR from scratch using Deep Learning and Convolutional Neural Networks
  • Application of natural language processing techniques in the texts extracted by OCR (word cloud and named entity recognition)
  • License plate reading

Reviews

  • A
    Aishwarya R
    4.0

    The course explains traditional OCR tools like Tesseract, EasyOCR, and EAST very well and is great for learning the fundamentals. One suggestion: it would really help learners if the course included a section on modern vision-LLM OCR methods (GPT-based OCR, Qwen2-VL, GOT-OCR2, LLaVA, etc.). These newer approaches are widely used now and give much better results for scanned or blurry documents. Adding a comparison between classical OCR and LLM-based OCR would make the course even more complete.

  • T
    Tiago Gonçalves Dias
    5.0

    Super clear to understand

  • R
    Roberto Rodriguez Apolinar
    5.0

    Really good explanation about the topics. Highly recommended course.

  • A
    Ahmed Abdullah Aafaq
    5.0

    The course is well structured and very well organized. It provides essential concepts that are required for individuals of various areas and expertise.

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