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EasyOCR: Ready-to-use OCR with 40 languages
- polote 6y agoWhat would be the advantage compared to something like Tesseract ?
- jonatron 6y agoTesseract isn't very accurate, especially with text in photos. It works OK for scanned documents, but that's about it.
- yorwba 6y agoI didn't see any accuracy comparison in the EasyOCR repo.
- albertzeyer 6y agoThis depends on the model you use, right? As far as I know, Tesseract supports a couple of models, and you could also use a more powerful neural network in there. And if you have trained it well, it should be fine.
- vmarius 6y agoAFAIK Tesseract is trained to recognize characters and uses a bunch of steps to prepare image for recognition. Steps like removing noise, fixing contrast and resizing. It means that it performs not-so-good when for example image contains black text and white text on green background since this is not "normalized" through image preparation steps and it cannot detect white text on green background (but you can do it yourself)
- rexreed 6y agoAre there any OCR benchmarks?
- sireat 6y agoTesseract can be very accurate (>99%), especially when you train it for your particular data set. This does involve creating your own labeled data. I got this 99% accuracy by performing incremental training using latest Manheim model as a base. I added about 20k lines which is not really that much. https://github.com/tesseract-ocr/tesseract/wiki https://github.com/tesseract-ocr/tesseract/wiki The hard part was crowd sourcing those 20k lines :) Tesseract might not be best for photos as you said but I did not have major problems. Of course some documents the source is so bad that a human can't achieve 99%. Tesseract used to be quite average before they moved onto LTSM models a few years ago.
- dclusin 6y agoCare to share resources/lessons learned for training tesseract with custom data? I'm using it for a side project and would love to hear about your insights.
- sireat 6y agoI followed the resources here: https://github.com/tesseract-ocr/tessdoc/blob/master/TrainingTesseract-4.00.md https://github.com/tesseract-ocr/tessdoc/blob/master/Trainin... Also this: https://github.com/UB-Mannheim/tesseract/wiki https://github.com/UB-Mannheim/tesseract/wiki The original data was here: https://github.com/tesseract-ocr/langdata_lstm https://github.com/tesseract-ocr/langdata_lstm I did use another data source from Manheim but can't locate it right now. Using vanilla Ubuntu 18.04 I looked at the example training files and made a small script to convert my own labeled data to fit the format that tesseract requires. I did do a bit of pre-processing adjusting contrast. All the data munging was done on Python (Pillow for image processing, Flask for collecting data into a simple SQLite DB before converting back to format that Tesseract requires). Python was not necessary just something that felt most comfortable to me. I am sure someone could do it using bash scripts or node.js or anything else. EDIT: To make life easier for my curators I did run Tesseract first to generate prelabeled data for my training set. It was about 90% accurate to start with. So the process was: Tesseract OCR on some documents to be trained -> hand curation (2 months)-> train (took about 12 hours) -> 99% (on completely separate test set)
- oefrha 6y agoLooking at the Chinese example, it’s kinda funny it managed to output Traditional Chinese characters when the image contains Simplified Chinese; the SC and TC versions look pretty different (园 vs 園, 东 vs 東).
- hawkice 6y agoYeah, a bit strange considering traditional and simplified make up two different languages in their list of forty.
- kevin_thibedeau 6y agoThey're rendering Unicode without any markup for language variant.
- oefrha 6y agoNo, these are completely different, standalone code points, not variant forms of the same code point. What's actually happening seems to be that the ch_tra model can recognize simplified too and output the corresponding traditional version if the character isn't in the traditional "alphabet"; it doesn't work so well in the other direction. Example recognizing a partial screenshot of https://chinese.stackexchange.com/a/38707 https://chinese.stackexchange.com/a/38707 (anyone can try this on Google Colab, no hardware required; remember to turn on GPU in Runtime -> Change runtime type): import easyocr import requests zhs_reader = easyocr.Reader(['en', 'ch_sim']) zht_reader = easyocr.Reader(['en', 'ch_tra']) image = requests.get('https://i.imgur.com/HtrpZCZ.png').content print('ch_sim:', ' '.join(text for _, text, _ in zhs_reader.readtext(image))) print('ch_tra:', ' '.join(text for _, text, _ in zht_reader.readtext(image))) Results: ch_sim: One simplified character may mapping to multiple traditional ones: 皇后->皇后,後夭->后夭 豌鬟->头发,骏财->发财 As reversed, one traditional character may mapping to multiple simplified ones too: 乾燥->干燥, 乾隆->乾隆 嘹望->嘹望,嘹解->了解 ch_tra: One simplified character may mapping to multiple traditional ones: 皇后->皇后,後天->后天 頭髮->頭發,發財->發財 As reversed, one traditional character may mapping to multiple simplified ones too: 乾燥->干燥, 乾隆->乾隆 瞭望->瞭望, 瞭解->了解 Compare to the original text: One simplified character may mapping to multiple traditional ones: - 皇后 -> 皇后，後天 -> 后天 - 頭髮 -> 头发，發財 -> 发财 As reversed, one traditional character may mapping to multiple simplified ones too: - 乾燥 -> 干燥，乾隆 -> 乾隆 - 瞭望 -> 瞭望，瞭解 -> 了解 Of course, automatic character-to-character conversion from simplified to traditional can be wrong due to ambiguities; excellent examples from above: 头发 => 頭發 (should be 頭髮), 了解 => 了解 (should be 瞭解).
- joelthelion 6y agoHas anyone tried it? How good is it?
- MayeulC 6y agoIt doesn't work that bad on a few French examples I had lying around: It's doing quite well on scanned documents, even quite dense ones. Handwriting doesn't work well at all, even for simpler cases. It managed to recognize a few words from a blackboard picture, but that's hardly usable. However, it looks like my simple example of an old "S note" export (like a lowish resolution phone screenshot) confused it a bit: Reglementation -> Reglemantation km -> kn illimitée -> illiritée limite -> liite baptême -> bapteme etc. Overall, it works, and it is quite easy to install and use. I'd have to compare it with tesseract, but I think it's a bit better. A lot slower, though (I only have AMD devices, no CUDA). It's underusing my CPU, and maybe leaking memory a bit, though I didn't clean up. Take that with a grain of salt, that was a quick try, I haven't tried to tune anything.
- williesleg 6y agoJust look at your photo in TikTok and it'll show you ads with your photo already OCR'd
- siver_john 6y agoI've recently become interested in OCR due to using Kaku on Android for trying to get better at reading Japanese. So thanks Hacker News for showing me a new version. I'd love any comments about other resources that may be good for learning. Especially because for funsies I'd like to try and develop my own.
- yorwba 6y agoFor learning, you could try training yourself on datasets for handwritten character recognition: http://etlcdb.db.aist.go.jp/ http://etlcdb.db.aist.go.jp/
- siver_john 6y agoThanks! A dataset was one of the things I was dreading searching for/building. (Maybe this was super easily searchable and I'm just a goon, again I'm early stages of passing interest).
- pjc50 6y agoI'm going to test this on my shopping receipts, if I can get over the Windows packaging hassle, and report back ..
- dclusin 6y agoFrom what I can tell (without having read the research papers) it looks like this is just an easy to use package for sparse scene text extraction. It seems to do okay if the scene has sparse text but it falls down for dense text detection. The results are going to be pretty bad if you try and do a task like "extract transactions from a picture of a receipt." Here's an example of input you might get for a production app: https://www.clusin.com/walmart-receipt.jpg https://www.clusin.com/walmart-receipt.jpg Notice the faded text from the printer running out of ink and the slanted text. From limited experience each of these are thorny problems and the state of the art CV algorithms won't help you escape from having to learn how to algorithmicly pre-process images and clean them up prior to feeding them into a CV algorithm. You might be able to use Google's Cloud OCR but that charges per image, although it is pretty good. Even if you use that you've graduated to the next super difficult problem which is Natural Language Processing. Once you have the text you need to determine if it has meaning to your application. That's basically what NLP is about. For the receipts example, how do you know you're looking at a receipt? What if its a receipt on top of a pile of other receipts? How do you extract transactions from the receipt? Does a transaction span multiple lines? How can you tell? etc etc etc.
- jonathanstrange 6y agoSo your point is that this library is not a magic unicorn that solves all problems related to OCR and natural language processing?
- nacs 6y agoTry reading the post. There’s a lot more there but the gist is that this is optimized for a different set of OCR uses and not the more typical scan a book/receipt cases.
- dclusin 6y agoThis is a fair point. I think my criticism more generally is that they position it as easy to use but its still just another library for a subset of OCR problems: sparse text extraction from a scene. As I said in a sibling post there doesn't seem to be a library that stitches together OCR approaches for all the different use cases and chooses an approach based on analyzing the image itself. That would be truly easy to use.
- zurn 6y agoDoes this require a Nvidia GPU? Some modules seem to import PyTorch and Cuda libs: https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/detection.py#L2 https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/dete...
- baicunko 6y agoThis is something that I find really interesting. Open-source OCR is lagging behing commercial applications and seeing someone trying ideas is always beneficial. Kudos!!
- deleted 6y ago[deleted]
- dharma1 6y agothis is great, been waiting for a newer machine learning based library as an option to Tesseract. a bit out of topic - but does anyone happen to know if there is an open-source, new school OCR library for music notation?
- unnouinceput 6y agoCompared to ABBYY, how does this thing fare? I don't have the time right now to do this test and if anyone here did it I'd be thankful to share.
- eastendguy 6y agoIt can not compete with cloud services from Abbyy, Google, OCR.space and others. But it runs locally and is open-source. It works for sparse text on images, and for that specific use case it is better than Tesseract.
- unnouinceput 6y agoI have Abby installed locally as well. I don't use it's cloud components. And I can set my own server exposing ABBYY's API's to rollout my own cloud server instead of theirs, if needs be.
- say_it_as_it_is 6y agoThe README presents an unfinished example. How does one work with the result?
- porker 6y agoWhat are people using in mobile development (native iOS/native Android/Cross-platform e.g. React-Native) when you want accurate extraction from a fixed format-source? E.g. poor-quality images of ID cards or credit cards, where the position of data is known.
- dclusin 6y agoOpenCV is excellent for this.
- tastyminerals 6y agonot on mobile but as a service -- Abbyy is the market leader in OCR from documents.
- oefrha 6y agoiOS has the Vision framework, can’t say whether it’s accurate enough for your use case. https://developer.apple.com/documentation/vision/recognizing_text_in_images https://developer.apple.com/documentation/vision/recognizing...
- gooftop 6y agoAnyone know how this (EasyOCR) compares with a service like AWS Textract?
- saradhi 6y agoWhat metrics do you want the comparison on? Cost: AWS is not free vs Open Sourced Time: AWS averages under 10 seconds vs 140 seconds on a standard Dell 7480 & 9 seconds on a GPU Google colab Character Accuracy: Almost same on a high quality input. No comparison with AWS on a blurred camera photo like this https://github.com/ExtractTable/ExtractTable-py/blob/master/samples/BlurryImage.jpg https://github.com/ExtractTable/ExtractTable-py/blob/master/...
- geonnave 6y agoFrom the title I thought it was OCR implemented in 40 programming languages.
- Yajirobe 6y agoHow do I train this on my own custom dataset/font?
- barbs 6y agoHas anyone made a desktop app with a really simple UI for detecting text in images? I'm thinking something that lives in the taskbar, lets you make a box around the text you want to read, and then returns it as plaintext? In my job as a support engineer I sometimes get screenshots of complex technical configurations and end up having to type them in one character at a time, so this would be really handy. Looks like maybe I could just create a wrapper around EasyOCR.
- jackson1way 6y agoI have the same problem every day. 20 years ago I used to have some tiny shareware tool for this but I lost it. Hope someone is able to recommend one!
- nelcevest 6y agoI was looking exactly for the same thing, and found this wonderful script: http://askubuntu.com/a/280713/81372 http://askubuntu.com/a/280713/81372 I put it in a custom keyboard shortcut, so I just press it, draw an onscreen rectangle around any non-selectable text, and in a few seconds it goes to the clipboard.