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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!
4.4
4.4/5
(15 reviews)
416 students
Created by

9.4

CourseMarks Score®

10.0

Freshness

8.0

Feedback

9.6

Content

Platform: Udemy
Video: 12h 57m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.4 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 4/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.0 / 10
We analyzed factors such as the rating (4.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.6 / 10
Video Score: 9.6 / 10
The course includes 12h 57m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

3 articles.
0 resource.
0 exercise.
0 test.

Table of contents

Description

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!

You will 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

Requirements

• Programming logic
• Python programming basic

This course is for

• Anyone interested in OCR (Optical Character Recognition)
• Undergraduate students who are studying subjects related to Artificial Intelligence, Digital Image Processing or Computer Vision
• Data Scientists who want to increase their knowledge in Computer Vision
• Professionals interested in developing professional optical character recognition solutions
• People interested in creating their own custom OCR

How much does the Optical Character Recognition (OCR) in Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 25% discount on the original price of the course, which was $19.99. So you save $5 if you enroll the course now.

Does the Optical Character Recognition (OCR) in Python course have a money back guarantee or refund policy?

YES, Optical Character Recognition (OCR) in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Optical Character Recognition (OCR) in Python course, but there is a $5 discount from the original price ($19.99). So the current price is just $14.99.

Who is the instructor? Is Jones Granatyr a SCAM or a TRUSTED instructor?

Jones Granatyr has created 79 courses that got 31,821 reviews which are generally positive. Jones Granatyr has taught 143,894 students and received a 4.7 average review out of 31,821 reviews. Depending on the information available, Jones Granatyr is a TRUSTED instructor.
Professor
Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.

9.4

CourseMarks Score®

10.0

Freshness

8.0

Feedback

9.6

Content

Platform: Udemy
Video: 12h 57m
Language: English
Next start: On Demand

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