Book Image

Python Web Scraping - Second Edition

By : Katharine Jarmul
Book Image

Python Web Scraping - Second Edition

By: Katharine Jarmul

Overview of this book

The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.
Table of Contents (10 chapters)

Optical character recognition

Optical character recognition (OCR) is a process to extract text from images. In this section, we will use the open source Tesseract OCR engine, which was originally developed at HP and now primarily at Google. Installation instructions for Tesseract are available at https://github.com/tesseract-ocr/tesseract/wiki. The pytesseract Python wrapper can be installed with pip:

pip install pytesseract

If the original CAPTCHA image is passed to pytesseract, the results are terrible:

>>> import pytesseract 
>>> img = get_captcha_img(html.content)
>>> pytesseract.image_to_string(img)
''

An empty string was returned, which means Tesseract failed to extract any characters from the input image. Tesseract was designed to extract more typical text, such as book pages with a consistent background. If we want to use Tesseract effectively, we will need to first...