Word level extraction only On Tuesday 13 February 2024 at 11:10:03 UTC+5:30 Santhiya C wrote:
> I had completed the training portion utilising the training tesseract OCR. > After annotating the.box file, it did not change the misspelt character for > my output extraction. > > I was followed this article only Training Tesseract-OCR with custom > data. | by Sai Ashish | Medium > <https://saiashish90.medium.com/training-tesseract-ocr-with-custom-data-d3f4881575c0> > how do i resolve this issue > On Thursday 8 February 2024 at 10:22:40 UTC+5:30 aromal...@gmail.com > wrote: > >> are you working on a word level text extraction or sentence level text >> extraction? >> >> On Tuesday 6 February 2024 at 12:11:03 UTC+5:30 santhi...@gmail.com >> wrote: >> >>> 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. 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