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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
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Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning Index

Chapter 8. Beating CAPTCHAs with Neural Networks

Interpreting information contained in images has long been a difficult problem in data mining, but it is one that is really starting to be addressed. The latest research is providing algorithms to detect and understand images to the point where automated commercial surveillance systems are now being used—in real-world scenarios—by major vendors. These systems are capable of understanding and recognizing objects and people in video footage.

It is difficult to extract information from images. There is lots of raw data in an image, and the standard method for encoding images—pixels—isn't that informative by itself. Images—particularly photos—can be blurry, too close to the targets, too dark, too light, scaled, cropped, skewed, or any other of a variety of problems that cause havoc for a computer system trying to extract useful information.

In this chapter, we look at extracting text from...

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