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

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In this Guided Project, you will:

Train Tensorflow to recognize a Region of Interest (ROI) in an image or frame of a video.

Extract and enhance relevant image segments with OpenCV .

Use Tesseract to extract, export text data for use in real-time.

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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. We’re currently working on providing the same experience in other regions.

Skills you will develop

TensorflowDeep Learning in PythonObject DetectionOptical Character RecognitionComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Set up a new Real Time Text Detection script

  2. Collect and Label Images for recognition of Region of Interest (ROI)

  3. Train Tensorflow to recognize Region of Interest (ROI)

  4. Capture webcam video stream, frames from a video file, or a static image

  5. Extract and enhance relevant image segments with OpenCV

  6. Use Tesseract to extract, export text data for use

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In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

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