can you please tell me model and steps On Monday 5 February 2024 at 17:22:10 UTC+5:30 aromal...@gmail.com wrote:
> if you are getting started with OCR try some other engines or just > start with some deep learning models > understand the basic working > On Thursday 1 February 2024 at 11:17:14 UTC+5:30 santhi...@gmail.com > wrote: > >> Already i was used above mentioned steps but i lost the datas >> >> On Saturday 27 January 2024 at 06:52:54 UTC+5:30 g...@hobbelt.com wrote: >> >>> L.S., >>> >>> *PDF. OCR. text extraction. best language models? not a lot of success >>> yet...* >>> >>> 🤔 >>> >>> Broad subject. Learning curve ahead. 🚧 Workflow diagram included today. >>> >>> >>> *Tesseract does not live alone* >>> >>> Tesseract is an engine, which takes an image as input and produces text >>> output; several output formats are available. If you are unsure, start with >>> HOCR output as that's close to modern HTML and carries almost all info >>> tesseract produces during the OCR process. >>> If it isn't an image you've got, you need a preprocess (and consequently >>> additional tools) to produce images you can feed tesseract. tesseract is >>> designed to process a SINGLE IMAGE. (Yes, that means you may want to >>> 'merge' its output: postprocessing) >>> >>> * To complicate matters immediately, tesseract can deal with >>> "multipage TIFF" images and can accept multiple images to process via its >>> commandline. Keep thinking "one page image in, bunch of text out" and >>> you'll be okay until you discover the additional possibilities.* >>> >>> *Advice Number 1: *get a tesseract executable, invoke it using its >>> commandline interface. If you can't build tesseract yourself, Uni Mannheim >>> may have binaries for you to download and install. Linuxes often have >>> tesseract binaries and mandatory language models available as packages, BUT >>> many Linuxes are more or less far behind the curve: latest tesseract >>> release as of this writing is 5.3.4: >>> https://github.com/tesseract-ocr/tesseract/releases so VERIFY your rig >>> has the latest tesseract installed. Older releases are older and "previous" >>> for a reason! >>> >>> >>> *Preprocessing is the chorus of this song* >>> >>> As you say "PDF", you therefor need to convert that thing to *page >>> images*. My personal favorite is the Artifex mupdf toolkit, using >>> mutool or mudraw / etc. tools from that commandline toolkit to render >>> accurate, high-rez page images. Others will favor other means but it all >>> ends up doing the same thing: anything, PDFs et al, is to be converted to >>> one image per page and fed to tesseract that way. The rendered page images >>> MAY require additional *image preprocessing*: >>> >>> >>> *This next bit cannot be stressed enough: *tesseract is designed and >>> engineered to work on plain printed book pages, i.e. BLACK TEXT on PLAIN >>> WHITE BACKGROUND. As I observe everyone and their granny dumping holiday >>> snapshots, favorite CD, LP and fancy colourful book covers straight into >>> tesseract and complaining "nothing sensible is coming out" that's because >>> you're feeding it a load of dung as far as the engine concerned: it expects >>> BLACK TEXT on PLAIN WHITE BACKGROUND like a regular dull printed page in a >>> BOOK, so anything with nature backgrounds, colourful architectural >>> backgrounds and such is begging for a disaster. And I only emphasize with >>> the grannies. <drama + rant mode off/> This is why >>> https://tesseract-ocr.github.io/tessdoc/ImproveQuality.html is >>> mentioned almost every week in this mailing list, for example. It's very >>> important, but you'll need more... >>> >>> >>> The take-away? You'll need additional tools for image preprocessing >>> until you can produce greyscale or B&W images that look almost as if these >>> were plain old boring book pages: no or very little fancy stuff, black text >>> (anti-aliased or not), white background. >>> Bonus points for you when your preprocess removes non-text image >>> components, e.g. photographs, in the page image: it can only confuse the >>> OCR engine so when you strive for perfection, that's one more bit to deal >>> with BEFORE you feed it into tesseract and wait expectantly... (Besides, >>> tesseract will have less discovery to do so it'll be faster too. Of little >>> importance, relatively speaking, but there you have it.) >>> As also mentioned at >>> https://tesseract-ocr.github.io/tessdoc/ImproveQuality.html : tools of >>> interest re image processing are leptonica (parts used by tesseract, but >>> don't count on it doing your preprocessing for you as it's a highly >>> scenario/case-dependent activity and therefor not included in tesseract >>> itself) Also check out: OpenCV (a library, not a tool, so you'll need >>> scaffolding there before you can use it), ImageMagick, (Adobe Photoshop or >>> open source: Krita: great for what-can-I-get experiments but not suitable >>> for bulk), etc.etc. >>> >>> >>> *Tesseract bliss and the afterglow: postprocessing* >>> >>> Once you are producing page images like they were book pages, and >>> feeding them into tesseract, you get output, being it "plain text", HOCR or >>> otherwise. >>> >>> Personally I favor HOCR but that's because it's closest to what *my >>> *workflow >>> needs. You must look into "postprocessing" anyway: being it additional >>> tooling to recombine the OCR-ed text into PDF "overlay", PDF/A production, >>> or anything else; advanced usage may require additional postprocessing >>> steps, e.g. pulling the OCR-ed text through a spellchecker+corrector such >>> as hunspell, if that floats your boat. You'll also need to get and set up >>> and/or program postprocess tooling if you otherwise wish to merge multiple >>> images' OCR results. You may want to search the internet for this; I don't >>> have any toolkit's name present off the top off my head for that as I'm >>> using tesseract in a slightly different workflow, where it is part of a >>> custom, *augmented *mupdf toolkit: PDF in, PDF + HOCR + misc document >>> metadata out, so all that preprocessing and postprocessing I hammer on is >>> done by yours truly's custom toolchain. Under development, so I'm not >>> working with the diverse python stuff most everybody else will dig up after >>> a quick google search, I'm sure. Individual project's requirements' >>> differences and such, so your path will only be obvious to you. >>> >>> >>> >>> *How to be trolling an OCR engine *😋 >>> >>> Oh, before I forget: some peeps drop shopping bills and such into >>> off-the-shelf tesseract: *cute *but not anything like a "plain printed >>> book page" so they encounter all kinds of "surprises": >>> https://tesseract-ocr.github.io/tessdoc/ImproveQuality.html is >>> important but it doesn't tell you *everything*. "plain printed book >>> pages" are, by general assumption, pages of text, or, more precisely: >>> *stories*. Or other tracts with paragraphs of text. Bills, invoices and >>> other financial stuff is not just "tabulated semi-numeric content" instead >>> of "paragraphs of text" but those types of inputs also fail grade F >>> regarding the other implicit assumption that comes with human "paragraphs >>> of text": the latter are series of words, technically each a bunch of >>> alphabet glyphs (*alpha*numerics), while financials often mix currency >>> symbols and numeric values: while these were part of tesseract's training >>> set, I am sure, they are not its focal point hence have been given less >>> attention than the words in your language dictionary. And scanning those >>> SKUs will fare even worse as they're just a jumbled *codes*, rather >>> than *language*. Consequently you'll need to retrain tesseract if your >>> CONTENT does not suit these mentioned assumptions re "plain printed book >>> page". Haven't done that yet myself; it's not for the faint of heart and >>> since Google did the training for the "official" tesseract language models >>> everyone downloads and uses, you can bet your bottom retraining isn't going >>> to be "nice" for the less well funded either. Don't expect instant miracles >>> and expect a long haul when you decide you must go this route [of training >>> tesseract], or you will meet Captain Disappointment. Y'all have been >>> warned. 😉 >>> >>> >>> >>> >>> *Why your preprocess is more important than kickstarting tesseract, by >>> blowing ether* up its carburetor* >>> >>> *Why is that "plain printed book page is like human stories and similar >>> tracts: paragraphs of text" mantra so important?* Well, tesseract uses >>> a lot of technology to get the OCR quality it achieves, including using >>> language dictionaries. While some smarter people will find switches in >>> tesseract where *explicit* dictionary usage can be turned off, it >>> cannot switch off the *implicit* use due to how the latest and best >>> core engine: LSTM+CTC (since tesseract v4) actually works: it slowly moves >>> its gaze across each word it is fed (jargon: *image segmentation >>> *preprocess >>> inside tesseract produces these word images) and LSTM is so good at >>> recognizing text, because it has "learned context": that context being the >>> characters surrounding the one it is gazing at right now. Which means LSTM >>> can be argued to act akin to a *hidden Markov model* (see wikipedia) >>> and thus will deliver its predictions based on what "language" (i.e. >>> *dictionary*) it was fed during training: human text which is used in >>> professional papers and stories. Dutch VAT codes didn't feature in the >>> training set, as one member of the ML discovered a while ago. Financial >>> amounts, e.g. "EUR7.95" are also not prominently featured in LSTMs training >>> so you can now guess the amount of confusion the LSTM will experience when >>> scanning across such a thing: reading "EUR" has it expect "O" with high >>> confidence, as in "eur" obviously leading to the word "euro", but what the >>> heck is that "digit 7" doing there?! That's *highly* unexpected, hence >>> OCR probabilities drop, pass decision-making thresholds and you get WTF >>> results, simply because the engine went WTF *first*. >>> Ditto story/drama for calligraphed signs outside shops, and, *oh! oh!, >>> license plates*!! (google LPR/ALPR if you want any of that) and *anything >>> else *that's *not *reams of text and thus you wouldn't expect to find >>> in a plain story- or textbook. >>> (And for the detail-oriented folks: yes, tesseract had/has a module on >>> board for recognizing math, but I haven't seen that work very well with my >>> inputs and not seen a lot of happy noises out there about it either, but >>> the Google engineer(s) surely must have anticipated OCRing that kind of >>> stuff alongside paragraphs of text. For us mere mortals, I'ld consider this >>> bit "an historic attempt" and forget about it.) >>> >>> >>> *Advice Number 2: *when rendering page images, the ppi (pixels per >>> inch) resolution to select would be best adjusted to produce regular lines >>> of text in those images where the capital-height of the text is around 30 >>> pixels. Typography people would rather like to refer to *x-height*, so >>> that would be a little lower in pixel height. Line height would be larger >>> as that includes stems and interline spacing. However, from an OCR engine >>> perspective, these (x-height & line-height) are very much dependent of the >>> font used and the page layout used, so they are more variable than the >>> reported optimal capital-D-height at ~32px. As no-one measures this >>> up-front, as an initial guess, 300dpi in the render/print-to-image dialog >>> of your render tool of choice would be reasonable start but when you want >>> more accuracy, tweaking this number can already bring some quality changes. >>> Of course, when the source is (low rez) bitmap images already (embedded in >>> PDF or otherwise), there's little you can do, but then there's still >>> scaling, sharpening, etc. image preprocessing to try. This advice is driven >>> by the results published here: >>> https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94/m/24JHDYQbBQAJ >>> (and google already quickly produced one other who does something like that >>> and published a small bit of tooling: >>> https://gist.github.com/rinogo/294e723ac9e53c23d131e5852312dfe8 ) >>> >>> >>> *) the old-fash way to see if a rusty engine will still go (or blow, >>> alas). Replace with "SEO'd blog pages extolling instant success with ease" >>> to take this into the 21st century.) >>> >>> >>> >>> *The mandatory readings list:* >>> >>> - https://tesseract-ocr.github.io/tessdoc/ImproveQuality.html >>> - https://tesseract-ocr.github.io/tessdoc/ >>> >>> >>> >>> >>> *The above in diagram form (suggested tesseract workflow ;-) )* >>> >>> [image: diagram.png] >>> (diagram PikChr source + SVG attached) >>> >>> >>> >>> 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, Jan 26, 2024 at 6:11 PM Santhiya C <santhi...@gmail.com> wrote: >>> >>>> Hi Guys , i will start development OCR using image and Pdf to text >>>> extraction what are the steps i need to follow , can you pleasse refer me >>>> the best model , already i had used the pytesseract engine but i did not >>>> get proper extraction ... >>>> >>>> Best Regards, >>>> >>>> Sandhiya >>>> >>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "tesseract-ocr" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to tesseract-oc...@googlegroups.com. >>>> To view this discussion on the web visit >>>> https://groups.google.com/d/msgid/tesseract-ocr/a92d17a9-4bcf-4ba0-a81c-71e8e08a4afen%40googlegroups.com >>>> >>>> <https://groups.google.com/d/msgid/tesseract-ocr/a92d17a9-4bcf-4ba0-a81c-71e8e08a4afen%40googlegroups.com?utm_medium=email&utm_source=footer> >>>> . >>>> >>> -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. To unsubscribe from this group and stop receiving emails from it, send an email to tesseract-ocr+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/tesseract-ocr/92b83313-380a-4ceb-9633-7659de322878n%40googlegroups.com.
