A few quick notes on performing OCR in Python using some popular engines and their insights and tips
Modern OCR systems
Optical Character Recognition (OCR) systems convert an image that contains valuable information (presumably in text format) into machine-readable data. In most cases, performing OCR in some of the available ways is the first step data extraction on paper or scan-based PDFs.
After a short web search, you’ll find plenty of links to a variety of open source and commercial tools, Google Vision and Tesseract OCR engines have had a long start over their competitors, especially in recent years.
Tesseract is offlineofe and an open source OCR engine with a full-featured API that can be easily implemented for any business project via some Python wrapper modules, pytesseract is one example.
On the contrary, Google Vision does not work locally, but rather on Google’s remote servers. To get started with the Google Vision API for your project, you’ll need to complete some configuration steps, including providing valid credentials. official guide. In addition, you may be charged for more than limited text recognition requests, as noted in Google’s pricing policy.
Despite fundamental differences in usage and options, both tools are of interest to virtually the same web user based on Google Trends:
As we progress, we try to run OCR in Python on both engines, randomly comparing their performance to real-life images (either created by the author or scanned to mimic original-quality documents).