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

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在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

TensorflowDeep Learning in PythonObject DetectionOptical Character RecognitionComputer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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