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I tested it out with a bunch of personal documents. Results were disappointing. Did not match up with the promised scores, not even slightly. I think the tradi
by AmazingTurtle 3y ago
I tested it out with a bunch of personal documents. Results were disappointing. Did not match up with the promised scores, not even slightly.
I think the traditional approach to scanning and classifying without AI/ML is the way to go, for the next 5 years at very least.
- mhitza 3y agoDo you (or anyone else) know which would be a good open source OCR for PDFs and images?
- lelandfe 3y agoOCRmyPDF is the typical answer: https://github.com/ocrmypdf/OCRmyPDF https://github.com/ocrmypdf/OCRmyPDF It uses Tesseract under the hood. Results tend to just be OK in my experience.
- xrd 3y agoTesseract is amazing. It is simple and generally good results.
- version_five 3y agoI was just playing with tesseract last week (I'd used it years ago) and wasn't too happy. I had a pretty simple pdf that was in what you could think of as an old typewritten font, but easily legible, and I got all kinds of word fragments and nonsense characters in the output. I know that high quality ocr systems include a language model to coerce the read text into the most probable words. Is tesseract just supposed to be the first stage of such a system? I'll note that when I put the tesseract output into chatgpt and prompted it saying it was ocr'd text and asking to clean it up, it worked very well.
- deleted 3y ago[deleted]
- flaviut 3y agoI was just processing a document with tesseract & ocrmypdf, and two things: My first time processing it, I used `ocrmypdf --redo-ocr` because it looked like there was some existing OCR. After processing, the OCR was crap because ocrmypdf didn't realize it was OCR but thought it was real text in the document that should be kept. This was fixable using `ocrmypdf --force-ocr`. Before realizing this, I discovered that Tesseract 4 & 5 use a neural network-based recognition. I then came across this step-by-step guide on fine-tuning Tesseract for a specific document set: https://www.statworx.com/en/content-hub/blog/fine-tuning-tesseract-ocr-for-german-invoices/ https://www.statworx.com/en/content-hub/blog/fine-tuning-tes... I didn't end up following the fine-tuning process because at this point `ocrmypdf --force-ocr` worked excellently, but I thought the draw_box_file_data.py script from their example was particularly useful: https://gist.github.com/flaviut/d901be509425098645e4ae527a9e9f3a https://gist.github.com/flaviut/d901be509425098645e4ae527a9e...
- denysvitali 3y agoFWIW, I'm using Google's ML Kit which runs completely on-device and doesn't send the documents to Google. It works better than tesseract for my use case. I did a presentation on the topic recently: https://clis-everywhere.k8s.best/16 https://clis-everywhere.k8s.best/16 I'll soon make the stack open source, but it shouldn't be hard to recreate given the inputs I've already provided.
- dataflow 3y agoI could never get tesseract to give good output. It baffles me when people say it's good. Do I need to tune it somehow or something?
- bobosha 3y agoeasyocr was the best of the bunch - however it still leaves quite a bit to be desired.
- wahnfrieden 3y agoNot open but free - Apple’s
- occamrazor 3y agoWhich Apple product?
- imaurer 3y agoTwo places I use it: Preview on my Mac, photos on my phone. Haven’t seen an api yet.
- momo93 3y agohttps://developer.apple.com/documentation/vision/recognizing_text_in_images https://developer.apple.com/documentation/vision/recognizing...
- vosper 3y agoIf you want to OCR specific text you can use Textsniper on Mac and draw a box on whatever part of the screen you want to capture. I'm guessing under the hood it's just using Apple's OCR tech, which does work very well (at least if you're on Apple Silicon, it's not quite so fast on my 2015 Intel Macbook Pro)
- wahnfrieden 3y agoVNRecognizeTextRequest and DataScannerViewController
- kcorbitt 3y agoWhen I was evaluating options a few months ago I found https://github.com/PaddlePaddle/PaddleOCR https://github.com/PaddlePaddle/PaddleOCR to be a very strong contender for my use case (reading product labels), but you'll definitely want to put together some representative docs/images and test a bunch of solutions to see what works for you.
- joaquincabezas 3y agoDid you finally select it? Our team is using PaddleOCR with good results! We wrote an article (https://medium.com/adevinta-tech-blog/text-in-image-2-0-improving-ocr-service-with-paddleocr-61614c886f93 https://medium.com/adevinta-tech-blog/text-in-image-2-0-impr...) about it.
- driscoll42 3y agoTessearct is generally the overall best for typed documents, though it struggles with handwriting. TrOCR is better than Tesseract, especially with handwriting, but requires a GPU to have any speed. Tesseract from my tests was roughly 200X faster than TrOCR (not an exaggeration)
- jstummbillig 3y agohttps://cloud.google.com/use-cases/ocr https://cloud.google.com/use-cases/ocr For my use cases, this already beats all "traditional approaches" for at least a few month now. That's just inferring from when I first stumbled across it. No clue for how long it's been a thing.
- j16sdiz 3y agoDo they feed your data to recaptcha ?
- threeseed 3y agoGoogle has scanned 40 million+ physical books and magazines Which it used OCR to produce digital text. So one source of training data at least.
- jstummbillig 3y agoI did not check. I also never checked if they share my mails on google search with you -- but I trust their ambition to not be sued into the ground for doing something immensely stupid. Leaking sensitive data of enterprise customers as training material for public recaptchas falls in that category.
- subbu 3y agohave you tried Azure's OCR? https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr https://learn.microsoft.com/en-us/azure/cognitive-services/c.... Is it comparable to Google's?
- driscoll42 3y agoI did some OCR tests on some 1960s era documents (all in English). Mix of typed and handwritten. I had as results: Google Vision: 95.62% HW - 99.4% Typed Amazon Texttract: 95.63% HW - 99.3% Typed Azure: 95.9% HW - 98.1% Typed Then if curious, TrOCR was the best FOSS solution at 79.5% HW and 97.4% Typed. (However it took roughly 200x longer than Tesseract which was 43% HW and 97.0% Typed)
- iamflimflam1 3y agoI think the online demos have been fine tunes to work on receipts.
- loudmax 3y agoDevelopments in this space are coming really fast, and reading words are squarely within the capabilities of neural engines. 5 years is a very long time in AI years.
- paddw 3y agoFor documents which are mostly pretty clean you are probably right. The ceiling for AI/ML is definitely higher though, and very useful right now if you know specifically what type of document you expect to look at, but expect it to be messy.
- jrpt 3y agoHow does it compare to something like https://docalysis.com https://docalysis.com?
- nogridbag 3y agoWhat would you recommend for classifying documents? Most of the companies I've evaluated market their product as using fancy AI/ML, but instead they have hundreds of people, usually in India, manually classifying the documents.
- refulgentis 3y agoI strongly believe everything just has to go through OpenAI or Anthropic, for now. These models are significantly better than any NLP models I try swapping in. But this isn’t much help if you must classify images.
- jameshart 3y agoFive years? Where’s that number coming from? This seems like exactly the kind of problem that will see rapid improvements as people point more LLMs at multimodal input. Right now making predictions for ML capabilities on a five year timeframe seems foolhardy.