PaddleOCR

I think the difficulty was specifically with CAPTCHA challenges, which had to be quick to generate but still legible - OCR on physical documents has to be robust to a different set of problems That said, what kind of errors are you getting? I think the main practical difference between Tesseract's LSTM-based OCR and newer VLM-based OCR like PaddleOCR [1] is (hopefully) getting to skip making heuristics for the layout of the document, but errors possibly compounding over multiple tokens - are you getting individual illegible words or having the documents smooshed together because the OCR can't parse the layout? [1] https://github.com/PaddlePaddle/PaddleOCR

This is essentially a (vibe-coded?) wrapper around PaddleOCR: https://github.com/PaddlePaddle/PaddleOCR The "guts" are here: https://github.com/majcheradam/ocrbase/blob/7706ef79493c47e8...

Any experience with Paddle OCR? https://github.com/PaddlePaddle/PaddleOCR Personally I‘ve used Tesseract before but the results were underwhelming, so I‘m curious how Paddle OCR performs in comparison.

What kind of accuracy have you reached with this pipeline of Tesseract+LLM? I imagine that there would be a hard limit as to what level the LLM could improve the OCR extract text from Tesseract, since its far from perfect itself. Haven't seen many people mention it, but have just been using the PaddleOCR library on it's own and has been very good for me. Often achieving better quality/accuracy than some of the best V-LLM's, and generally much better quality than other open-source OCR models I've tried like Tesseract for example. That being said, my use case is definitely focused primarily on digital text, so if you're working with handwritten text, take this with a grain of salt. https://github.com/PaddlePaddle/PaddleOCR/blob/main/README_e... https://huggingface.co/spaces/echo840/ocrbench-leaderboard

I tried https://github.com/PaddlePaddle/PaddleOCR for my own use case (scanline images of parcel labels) and it beat Tesseract by an order of magnitude. (Tesseract managed to get 3 fields out of a damaged label, while PaddleOCR found 35, some of them barely readable even for a human taking time to decypher them)

Was this by any chance Paddle OCR https://github.com/PaddlePaddle/PaddleOCR

Open Source Full Featured: https://github.com/Filimoa/open-parse/ [mine] https://docs.llamaindex.ai/en/stable/api_reference/node_pars... [text splitters lose page metadata] https://github.com/VikParuchuri/marker [strictly to markdown] Layout Parsers: These are collections of ML models to parse the core "elements" from a page (heading, paragraph, etc). You'll still need to work on combining these elements into queryable nodes. https://github.com/Layout-Parser/layout-parser https://github.com/opendatalab/PDF-Extract-Kit https://github.com/PaddlePaddle/PaddleOCR Commercial: https://reducto.ai/ [great, expensive] https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse... [cheapest, but buggy] https://cloud.google.com/document-ai https://aws.amazon.com/textract/

There is English version of the document provided by the official. https://github.com/PaddlePaddle/PaddleOCR/blob/main/README_e...

One good self-hosted OCR is PaddleOCR, https://github.com/PaddlePaddle/PaddleOCR Beats everything else, truly international and multi-lingual, including Chinese (as it is made in China)

If you want to run locally you can look into this https://github.com/PaddlePaddle/PaddleOCR https://andrejusb.blogspot.com/2024/03/optimizing-receipt-pr... But I suggest that you just skip that and use gpt-4o. They aren't actually going to steal your data. Sort through it to find anything with a credit card number or anything ahead time. Or you could look into InternVL.. Or a combination of PaddleOCR first and then use a strong LLM via API, like gpt-4o or llama3 70b via together.ai If you truly must do it locally, then if you have two 3090s or 4090s it might work out. Otherwise it the LLMs may not be smart enough to give good results. Leaving out the details of your hardware makes it impossible to give good advice about running locally. Other than, it's not really necessary.

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 -- don't think development is still active but the results are pretty great on Korean text

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).

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.