doctr
tessdata
doctr | tessdata | |
---|---|---|
12 | 10 | |
3,075 | 5,890 | |
5.7% | 1.0% | |
8.9 | 2.8 | |
1 day ago | about 2 months ago | |
Python | ||
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
doctr
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Show HN: How do you OCR on a Mac using the CLI or just Python for free
https://github.com/mindee/doctr/issues/1049
I am looking for something this polished and reliable for handwriting, does anyone have any pointers? I want to integrate it in a workflow with my eink tablet I take notes on. A few years ago, I tried various models, but they performed poorly (around 80% accuracy) on my handwriting, which I can read almost 90% of the time.
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Show HN: BetterOCR combines and corrects multiple OCR engines with an LLM
Yup! But I'm still exploring options. (any recommendations would be welcomed!) Here are some candidates I'm considering:
- https://github.com/mindee/doctr
- https://github.com/open-mmlab/mmocr
- https://github.com/PaddlePaddle/PaddleOCR (honestly I don't know Mandarin so I'm a bit stuck)
- https://github.com/clovaai/donut - While it's primarily an "OCR-free document understanding transformer," I think it's worth experimenting with. Think I can sort this out by letting the LLM reason through it multiple times (although this will impact performance)
- yesterday got a suggestion to consider https://github.com/kakaobrain/pororo - I don't think development is still active but the results are pretty great on Korean text
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OCR at Edge on Cloudflare Constellation
EasyOCR is a popular project if you are in an environment where you can use run Python and PyTorch (https://github.com/JaidedAI/EasyOCR). Other open source projects of note are PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR) and docTR (https://github.com/mindee/doctr).
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DeepDoctection
Last I checked I saw a grocery bill example using https://github.com/mindee/doctr and was fairly accurate. Bear in mind that was last year, hopefully it got even better or there are other libraries
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Confidential Optical Character Recognition Service With Cape
For its OCR service, Cape uses the excellent Python docTR library. Some of the critical benefits of docTR are its ease of use, flexibility, and matching state-of-the-art performance. The OCR model consists of two steps: text detection and text recognition. Cape uses a pre-trained DB Resnet50 architecture for detection, and for recognition, it uses a MobileNetV3 Small architecture. To learn more about the level of OCR accuracy you can expect for your document, you can consult these benchmarks provided by docTR. As you will see, model performance is very competitive compared to other commercial services.
- 👋 Unstable Diffusion here, We're excited to announce our Kickstarter to create a sustainable, community-driven future.
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Frog: OCR Tool for Linux
There's also DocTR which can do text detection and extraction out of the box.
It's command line driven but can display the detected text as an overlay of the document.
https://github.com/mindee/doctr
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OCRmyPDF: Add an OCR text layer to scanned PDF file
If you want to OCR a document image, modern versions of Tesseract can work well. If you last used it a few years ago, the recognition has improved since due to a new text recognition algorithm that uses modern (deep learning) techniques. Browser demo using a modern version: https://robertknight.github.io/tesseract-wasm/.
OCR processing typically consist of two major steps: detecting/locating words or lines of text on the page, and recognizing lines of text.
Tesseract's text recognition uses modern methods, but the text detection phase is still based on classical methods involving a lot of heuristics, and you may need to experiment with various configuration variables to get the best results. As a result it can fail to detect text if you present it with something other than a reasonably clean document image.
Doctr (https://github.com/mindee/doctr) is a new package that uses modern methods for both text detection and recognition. It is pretty new however and I expect will take more time and effort to mature.
- DocTR: Open-Source OCR Based on TensorFlow or PyTorch
- DocTR: A seamless, high-performing and accessible library for OCR-related tasks
tessdata
- Kubuntu use OCR
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Frog: OCR Tool for Linux
Appears to be a nice wrapper around Tesseract:
https://github.com/tesseract-ocr/tessdata
https://en.wikipedia.org/wiki/Tesseract_(software)
The demo of course works perfectly on a Mac as this is already built into Ventura.
In November 2020, Brewster Kahle from the Internet Archive praised Tesseract saying:
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My take on document archiving: Virtualpaper
The language packas are available at: https://github.com/tesseract-ocr/tessdata
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Has anyone used a RPI4 for OCR (Tesseract)? How did you feel about execution speed?
Then you could try different engines. --oem 0 disables the shiny new neural network stuff and uses the classic Tesseract engine; --oem 1 does the opposite. Try both, see which works best in terms of performance and accuracy for your particular use-case. You'll need to have training data for the legacy engine, though. These would work, or you could try tessdata_fast, which is specifically built for speed.
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Add more languages to your ServiceNow RPA OCR action
Go to the GitHub repo and get the trained data of your desired language --> https://github.com/tesseract-ocr/tessdata
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Is Capture2Text still the best OCR/Image text converter or are there any alternatives?
Capture2Text uses Google's Tesseract to perform OCR, so the accuracy when detecting foreign languages depends on the quality of the data files. Ones provided by Tesseract itself can be found here.
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PGS Subtitles Not Working in Web Browser
PGS graphical subtitle rendering is not supported in web browser. But we can convert it into text subtitle by using ML assisted OCR. https://github.com/tesseract-ocr/tessdata
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Hi, I built my first USEFUL docker setup. (it all works) Looking for advice on how it could be improved. It sets up an ubuntu machine to run a text recognition Flask server.
FROM ubuntu:18.04 RUN apt-get update RUN apt-get -y install curl RUN apt-get -y install wget unzip RUN mkdir tess5 RUN cd tess5 RUN wget https://github.com/tesseract-ocr/tesseract/archive/refs/tags/5.0.0-alpha-20210401.zip RUN unzip 5.0.0-alpha-20210401.zip RUN rm 5.0.0-alpha-20210401.zip WORKDIR /tesseract-5.0.0-alpha-20210401 RUN apt-get -y install autoconf automake libtool pkg-config libpng-dev libjpeg8-dev libtiff5-dev g++ # or clang++ (presumably) RUN apt-get install zlib1g-dev RUN apt-get -y install libleptonica-dev RUN ./autogen.sh RUN ./configure --enable-debug RUN LDFLAGS="-L/usr/local/lib" CFLAGS="-I/usr/local/include" make RUN make install RUN ldconfig WORKDIR /usr/local/share/tessdata RUN wget https://github.com/tesseract-ocr/tessdata/raw/master/eng.traineddata ENV TESSDATA_PREFIX=/usr/local/share/tessdata/
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Nifty little OCR script which I use a lot. Maybe one of you might make use of it as well.
You'll also need to go and get the trained language data from here: https://github.com/tesseract-ocr/tessdata/blob/master/eng.traineddata
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How do i use matlab ocr to recognize math equations?
The code looks fine, I think for whatever reason the 'MathEquations' network just does a poor job of recognizing the equations. The support package that includes the language is based on this open-source tessaract repo which seems to struggle with math equation recognition (at least based on this issue).
What are some alternatives?
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
tesseract-ocr-for-php - A wrapper to work with Tesseract OCR inside PHP.
tesserocr - A Python wrapper for the tesseract-ocr API
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.
Tesseract.js - Pure Javascript OCR for more than 100 Languages 📖🎉🖥
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
greenshot - Greenshot for Windows - Report bugs & features go here: https://greenshot.atlassian.net or look for information on:
react-native-tesseract-ocr - Tesseract OCR wrapper for React Native
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
Keycloak - Open Source Identity and Access Management For Modern Applications and Services