However, out of the box, Tesseract can be “good enough” for the purposes of extracting just enough text from an image in order to accomplish what you may need to do in your software application. You may also need to “massage” an input image before it could be read correctly. This is typically printing quality as opposed to web quality. Tesseract always works better with 300dpi (dots per inch) or higher images. Tesseract in particular may require additional “training” (its jargon) to be better at reading text data from images. There may be situations in which different OCR software may be necessary depending on the use case. Even with decades of hard work going into researching this, there are still instances in which OCR may not be the best solution for text extraction. OCR is Not a Silver Bulletīefore getting into the technical details, it is important to dispense with the idea that OCR can always magically read all of the text in an image. This software development tutorial will focus on implementing Tesseract within an Ubuntu Linux environment, since this is the easiest environment for a beginner to exploit. Tesseract is open-source and offered under the Apache 2.0 license which gives developers a wide berth in how this software can be included in their own offerings. Google’s Tesseract offering gives software developers access to a “commercial grade” OCR software at a “bargain basement” price. 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. We may make money when you click on links to our partners. content and product recommendations are editorially independent.
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