Text extractor examples software#

But how would a developer integrate this technology into his or her own software project?

Recognizing the text in these photos has become a standard feature of most photo management software. They simply take photos of the slides they are interested in and save them for later. Consumers of information typically do not want to download PowerPoint presentations and search through them. The use of screenshots as a note-taking method changed that trajectory. OCR – or Optical Character Recognition – had been quite a hot topic in the long-past days of digitizing paper artifacts such as documents, newspapers and other such physical media, but, as paper has gone by the wayside, OCR, while continuing to be a hot research topic, briefly moved to the back burner as a “pop culture technology.” You can read more about image recognition in our tutorial: Image Recognition in Python and SQL Server. We will be covering this topic in today’s Python programming tutorial. Extracting the text would allow for the text to be indexable and searchable. Optical Character Recognition (OCR) takes this a step further, by allowing developers to extract the text presented in an image.

This topic was covered in the previous article Image Recognition in Python and SQL Server, in which a solution to programmatically identifying an image by its contents was presented. Machine Vision has come a long way since the days of “how can a computer recognize this image as an apple.” There are many tools available that can easily help to identify the contents of an image.

Text extractor examples