tessdata
PaddleOCR
tessdata | PaddleOCR | |
---|---|---|
10 | 60 | |
5,934 | 38,878 | |
1.8% | 3.2% | |
2.8 | 8.7 | |
2 months ago | 1 day ago | |
Python | ||
Apache License 2.0 | Apache License 2.0 |
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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).
PaddleOCR
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Leveraging GPT-4 for PDF Data Extraction: A Comprehensive Guide
PyTesseract Module [ Github ] EasyOCR Module [ Github ] PaddlePaddle OCR [ Github ]
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What is the best repo for hand written text recognition?
My default recommendation for OCR is https://github.com/PaddlePaddle/PaddleOCR but most of the examples there are not handwritten - so I'm not sure how well it'll handle it this time.
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Ask HN: Best way to perform complex OCR task in 2023?
Other than EasyOCR and Tesseract, PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR) is probably the most well known open-source OCR solution.
What are you planning to do with the text after detecting / recognizing it? How fast does the detection / recognition need to be in order to be useful?
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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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How would you go about driving contextual data from images?
For images with text, if you want to do visual qa, document classification, table/key information extraction, checkout https://huggingface.co/blog/document-ai https://github.com/philschmid/document-ai-transformers https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/README.md
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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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Seeking Advice for Improving OCR Accuracy in a Code Snippet Reader Project
I think you can train tesseract with custom data if you have enough, or you can use deep learning models like https://pyimagesearch.com/2020/08/17/ocr-with-keras-tensorflow-and-deep-learning or https://www.google.com/amp/s/nanonets.com/blog/attention-ocr-for-text-recogntion/amp/ or try other existing tools like paddle-ocr https://github.com/PaddlePaddle/PaddleOCR
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How do you parse tables in PDF with langchain? Especially, the context which is few lines above and below the table.
https://huggingface.co/blog/document-ai https://github.com/microsoft/table-transformer https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/table/README.md
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unable to install paddleocr on m1 mac
when following the installation commands present in the paddleocr repo(https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_en/quickstart_en.md) im still unable to install paddleocr. paddlepaddle is successfully installed on my m1 mac with python3.9.16 but while installing paddleocr im getting this error after long pip backtracking
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Donut: OCR-Free Document Understanding Transformer
When I was evaluating options a few months ago I found 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.
What are some alternatives?
tesseract-ocr-for-php - A wrapper to work with Tesseract OCR inside PHP.
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
Tesseract.js - Pure Javascript OCR for more than 100 Languages 📖🎉🖥
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
doctr - docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
greenshot - Greenshot for Windows - Report bugs & features go here: https://greenshot.atlassian.net or look for information on:
OCRmyPDF - OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.