:+1: Glad it works out so well for you!
Met vriendelijke groeten / Best regards, Ger Hobbelt -------------------------------------------------- web: http://www.hobbelt.com/ http://www.hebbut.net/ mail: g...@hobbelt.com mobile: +31-6-11 120 978 -------------------------------------------------- On Thu, Feb 23, 2023 at 12:47 PM Alex Porter <trumpetdu...@gmail.com> wrote: > Thanks Ger, this has been incredibly helpful! Reducing the image size for > OCR has dramatically increased the accuracy and reliability of my output. > > On Wed, Feb 22, 2023 at 11:31 AM Ger Hobbelt <g...@hobbelt.com> wrote: > >> Re the line pixel height research I mentioned I recalled: it's here: >> https://willus.com/blog.shtml?tesseract_accuracy and here: >> https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94 >> >> I had forgotten I got it from this mailinglist! >> >> On Sat, Feb 18, 2023, 18:39 Ger Hobbelt <g...@hobbelt.com> wrote: >> >>> Hi, >>> >>> Had a very quick look but got sidetracked into something else, so I >>> didn't write the tesseract test script I wanted, so TILAAEFTR. Here goes: >>> >>> your '4' output image is rather large for tesseract to treat it as a >>> 'single line'. >>> >>> tess is known to deliver different accuracies for (*wildly*) different >>> line sizes -- I seem to recall some research and graphs from 2019 where >>> accuracy went down for both too small (8-10px) and *way too high* (200+px), >>> producing a bit of /skewed/ bathtub curve for the OCR error rate, so the >>> idea here is to rescale your extracted number images to a suitable size, >>> before feeding it ot the OCR engine. >>> >>> Test this remark/idea with a script: >>> >>> ``` >>> let img = 'out.png' // the '4', f.e. >>> for (let h = 8; h < 500; h = ceil( h * 1.1 /* = +10% */ )) { >>> /* use imagemagick for scaling, f.e.? */ >>> rescale(img, height: h, unit: 'px') -> img2 >>> tesseract(img2) -> txt >>> } >>> ``` >>> >>> (pseudocode above; write in your favorite scripting language: bash, js, >>> python, whatever) >>> >>> collect the `txt` OCR results; rank them and see where your 'optimum >>> height' lands you. Then use that for your application. >>> >>> >>> >>> Afterthought / Side thought: >>> >>> I see you are grabbing a computer display screen and applying OCR to it. >>> A few thoughts pop up immediately given the source type: >>> >>> I see a rather organized screen, no noisy/chaotic background you get >>> with burned-in subtitles, for example. Food for thought. >>> >>> - doesn't it suffice to take the number (*digit*) images and compare >>> them against a (created) master set, using a image similarity metric? As >>> it's the machine rendering those numbers, they should be pretty consistent, >>> save for some anti-aliasing or non-pixel-accurate positioning in the >>> renderer resulting in (slightly) different pixel values / images for each >>> digit. (Feels like tesseract is an elephant gun for this. But then I >>> probably missed several cues and be utterly wrong...) >>> >>> - of that same vein, taking it one further: since it's output from a >>> computer machine, can't we hook into the software which produces these >>> images and get the raw digital numeric / scoreboard data from the software >>> straight away? Iff we can, we don't have the significant overhead and data >>> accuracy challenges that come with reversing anything using OCR: it's never >>> a 100% accuracy this way. (software protections and other obstructions >>> related to data commerce and ~ politics can keep us at a distance, where >>> screengrabbing+OCR becomes an optimum viable solution if we want to get >>> access to the data, but I would love to get away with less for the same (or >>> better) result. :-S ) >>> >>> - is it me or am I seeing more of this machine -> >>> screengrab/scan/photograph, digitally or *analog* (phone snaps of other >>> phones' screens) -> machine OCR data transport queries lately ('22 / '23)? >>> Have I missed something? >>> >>> This looks like trade/score screens and at least the traders would have >>> *some* incentive to provide an API for this. (When you find the related >>> paywall insurmountable, grab+OCR is the way to go, alas, but it will always >>> be somewhat finicky.) >>> >>> >>> >>> Met vriendelijke groeten / Best regards, >>> >>> Ger Hobbelt >>> >>> -------------------------------------------------- >>> web: http://www.hobbelt.com/ >>> http://www.hebbut.net/ >>> mail: g...@hobbelt.com >>> mobile: +31-6-11 120 978 >>> -------------------------------------------------- >>> >>> >>> On Fri, Feb 17, 2023 at 8:08 AM Alex Porter <trumpetdu...@gmail.com> >>> wrote: >>> >>>> am currently building a pythont tool to read the screenshots of a >>>> in-game scoreboard. The scoreboard looks like this:[image: ss_1.png] >>>> >>>> I am using open cv to analyse the scoreboard and can reliably slice the >>>> image into rows and extra each value from the scoreboard giving an image, >>>> after processing, like this:[image: crop3.png] >>>> >>>> I am still having issues with tesseract accurately identifying the >>>> numbers. Sometimes it is inaccurate (identifying the wrong number) or not >>>> giving any output at all. I have only whitelisted 0-9 when reading the >>>> numbers. Any help on pre-processing the image to increase accuracy or any >>>> other ideas would be much appreciated! >>>> >>>> I have also attatched the python code. 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