If the letters are small and fuzzy, OCR has a difficult time determining exactly what characters they are. The clearer the text, the more likely OCR will succeed. There is a single, simple characteristic of text in an image that determines how accurate OCR will be: its clarity. Even though OCR has improved as computers have become more powerful, it still introduces errors into its results. However, the analysis to identify individual characters can make mistakes. OCR is a computer analysis of that image. That image - most commonly in “.jpg” or “.png” format - contains text you want to be able to use in some other context without having to re-type it all. Rather than retyping that text by hand to use elsewhere, you can use OCR to automatically extract the text for you.Īs it turns out, Microsoft OneNote, present in Windows 10 and Microsoft Office, has basic OCR capability built in. Or you might scan a document you’ve received, which often results in a series of image files, or a PDF containing a series of images - “pictures”, if you will, of the individual pages. All four were overwhelmingly better than Acrobat and Google Docs, which had embarrassingly poor results.OCR, an acronym for Optical Character Recognition, is a process that converts a picture of text into actual, editable, text.įor example, you might find a picture of a meme on social media, which may be nothing more than text on a nice background saved in an image format, such as. Evernote shows matches within the text as you type and appeared to rival Monterey and PDFpen. OneNote, once Microsoft had performed its delayed recognition, was quite close to those two as well. PDFpen and macOS Monterey’s Live Text performed extremely accurately.
With the macOS Monterey beta four, Apple enabled Live Text functionality on Intel Macs. Also, Apple notes as a footnote on the macOS Monterey preview website that an M1 is required.
My testing involved using the public beta of macOS Monterey. You probably won’t be performing text extraction against 1920s magazine articles-maybe so, if you’re like me!-but the slightly degraded nature of the source text and quality of the scan puts the services and software to a more substantial test than pristine rendered typography. You can see the figures below with each app or service noted. For a side-by-side comparison that demonstrated my results starkly, I copied out the results of recognition against the same legibly typeset magazine copy from a 1920s Popular Mechanics article (about comic-strip production). In researching this article, I tested a range of images and documents that proved fairly consistent across each service or app.
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