mayan-edms VS PaddleOCR

Mayan EDMS / Mayan EDMS · GitLab

I always love open-source projects for their quality and the amazing community who build it through their contributions. Whenever I'm in need of any professional software for day-to-day productivity or hobby projects for personal use, I always search in google along with "open-source" keyword. I found so many such open-source projects which I end up using very frequently. apps.diagrams.net (formerly draw.io), Greenshot, Mayan EDMS, WinMerge, Notepad++, Visual Studio Code are to name a few.

I'm trying to run Mayan EDMS in a docker container and I'm using the official docker compose from here: https://gitlab.com/mayan-edms/mayan-edms/-/blob/master/docker/docker-compose.yml

As far as actual solutions to my original question...looks like Mayan actually does support transparent encryption for storage and transport starting with version 3.4 (and mentioned here) but it's virtually undocumented as far as I can tell.

PyTesseract Module [ Github ] EasyOCR Module [ Github ] PaddlePaddle OCR [ Github ]

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.

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?

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

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

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).

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

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

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

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.