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. 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