Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract from Coursera

In this 1-hour long project-based course, you will learn how to collect and label images and use them to train a Tensorflow CNN (convolutional neural network) model to recognize relevant areas of (typeface) text in any image, video frame or frame from webcam video. You will learn how to extract image segments that your detector has identified as containing text and enhance them using various image filters from the OpenCV module. Then you will learn how to pass the result image to Google's open-source OCR (Optical Character Recognition) software using the pytesseract python library and read the text to whatever form of output you like. All of this will be done on Windows, but can be accomplished with very little alteration on Linux as well. We will be using the IDLE development environment to write a single script to scan our video, webcam input, or array of images for text and read that text into our output. Tensorflow, the Tensorflow Object Detection API, Tesseract, the pytesseract library, labelImg for image annotation, OpenCV, and all other required software has already been installed for you in your Rhyme desktop. Note: This course works best for learners who are based in the North America region. Weâ€™re currently working on providing the same experience in other regions.

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Provides hands-on practice with image processing and OCR

Introduces learners to several libraries such as TensorFlow, OpenCV, and tesseract

Develops skills in image annotation, image enhancement, and text recognition

Provides a comprehensive introduction to text recognition in images using TensorFlow and OpenCV

Requires learners to have a background in Python and basic image processing concepts

Assumes learners have access to Windows or Linux operating systems and specific software

Reviews summary

Real-time ocr with tensorflow, opencv and tesseract

This 1-hour long course focuses on using Tensorflow, OpenCV, and Tesseract for real-time OCR and text detection. As it is project-based, learners will work on a single script to scan video, webcam input, or an array of images for text. Students should ideally have some familiarity with Python and a basic knowledge of Tensorflow. The course works best for learners in the North America region.

The emphasis is on coding.

"Just comment a few lines of code."

"It includes a lot of pre-coded modules and it is not possible to run them on our own PC."

"I did not like the approach of typing all the code"

Access to the platform may be limited.

"The platform is not helpful to practice and there is no support from RHYME."

"The cloud environment has a problem."

The course lacks detailed explanations.

"Almost no intuitive explanations "

"It keeps changing automatically, you have to stop coding, pause the video and click on the button next to the Fit one."

"No explanation indetails"

Expect a challenging course.

"The course is very poorly organised."

"NEED TO HAVE A DEEP PREVIOUS KNOWLEDGE ON PYTHON AND SOME OTHER STUFF TO UNDERSTAND THE CORE OF THIS."

"Too fast, explanation is too vague and the guy seems to assume that we are already too well versed with this subject."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract with these activities:

Follow tutorials on Tensorflow and OpenCV

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Following tutorials on Tensorflow and OpenCV will provide you with a solid foundation in these technologies, which will be essential for completing the course projects.

Browse courses on TensorFlow

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Find tutorials on Tensorflow and OpenCV
Follow the tutorials step-by-step
Complete the exercises

Review 'Deep Learning with Python'

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Reviewing 'Deep Learning with Python' will provide you with a deeper understanding of the concepts behind Tensorflow and deep learning, which will be beneficial for completing the course projects.

View Deep Learning with Python, Second Edition on Amazon

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Read the book
Work through the exercises
Apply the concepts to the course projects

Implement a Tensorflow CNN to detect text

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Creating a Tensorflow CNN to detect text will solidify your understanding of the course material and help you develop practical skills in computer vision.

Browse courses on TensorFlow

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Collect images with text
Annotate the images with text labels
Train a Tensorflow CNN model to detect text
Evaluate the performance of the model

Four other activities

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Practice extracting text from images

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Practicing extracting text from images will help you develop proficiency in using OpenCV and other computer vision tools.

Browse courses on Computer Vision

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Use OpenCV to load an image
Convert the image to grayscale
Apply image processing techniques to enhance the text
Use OCR to extract the text

Start a project to build a text detection application

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Starting a project to build a text detection application will give you hands-on experience in applying the concepts you learn in the course.

Browse courses on Computer Vision

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Define the scope of the project
Gather the necessary resources
Develop a plan for the project
Start working on the project
Complete the project

Create a blog post or video tutorial on text detection

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Creating a blog post or video tutorial on text detection will help you solidify your understanding of the concepts and share your knowledge with others.

Browse courses on Computer Vision

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Choose a topic for your blog post or video tutorial
Research the topic
Write or record your blog post or video tutorial
Publish or share your blog post or video tutorial

Participate in a Kaggle competition on text detection

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Participating in a Kaggle competition on text detection will give you the opportunity to apply your skills to a real-world problem and compete with other data scientists.

Browse courses on Computer Vision

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Find a Kaggle competition on text detection
Join the competition
Develop a solution to the competition
Submit your solution
Evaluate your results

Career center

Learners who complete Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract will develop knowledge and skills that may be useful to these careers:

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See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers work with large amounts of data to train machine learning models to solve business challenges. They may also build and maintain the infrastructure that supports these models. This course provides a foundation in machine learning concepts and techniques that are essential for success in this role. It also covers topics such as image processing and natural language processing, which are important for many machine learning applications.

See salaries and explore the career path for Machine Learning Engineer

AI Engineer

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See salaries and explore the career path for AI Engineer

Software Engineer

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Image Processing Engineer

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Research Scientist

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Computer Vision Engineer

Computer Vision Engineers develop and maintain software that can interpret and understand images and videos. They may work on a variety of projects, such as facial recognition, object detection, and medical imaging. This course provides a foundation in computer vision concepts and techniques that are essential for success in this role. It also covers topics such as image processing and machine learning, which are important for many computer vision applications.

See salaries and explore the career path for Computer Vision Engineer

Natural Language Processing Engineer

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See salaries and explore the career path for Natural Language Processing Engineer

Financial Analyst

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Marketing Analyst

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User Experience Designer

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Business Analyst

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See salaries and explore the career path for Business Analyst

Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract

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Real-time ocr with tensorflow, opencv and tesseract

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