See this for a comparison of PaddleOCR, TrOCR, and various cloud ones (note: on documents of typed and handwritten text):

https://news.ycombinator.com/item?id=32077375

Caveat that being from 2022, the Tesseract version used was almost certainly v4 (if Linux), rather than v5 which is much better (and widely available on Windows in 2022, but not Linux yet).

However Tesseract is quite behind still as you note, even with v5.

Tesseract's true value is being one apt-get command away (i.e. opensource). Does Debian host more modern OCR systems in their repos?

Big mistake on my part; I should clarify I fine-tuned both PaddleOCR and TrOCR on large amounts of data specific to my domain. I cannot speak on the best out of the box “ready to go” solutions (besides cloud ones, which were quite good with the right pre and post processing).

Tesseract the tool is one apt-get away but the trained models are not, and I've found that they are a starting point, not a final destination. You still have to do more training on top of them for anything that isn't black text on a crisp white background.

Running PaddleOCR in production now, I would suggest contrasting Tesseract v4 and v5, since v5 is a lot better(but until recently has not been available on Linux) - PaddleOCR does still smoke it though, you are right (especially for concurrency and fairly easily just setting different workers to different GPUs for best concurrent batching).

How is Paddle on complex data tables? This is my biggest challenge at the moment.

What format? The entire data table in one image, or a PDF for example printed off with 8 pages where the user choose to only put the header on the first page etc? Or decent formatting, font size 8+ on an image with decent resolution? With the latter you are probably fine although you will need some manual implementation for parsing the output. You get bounding boxes at word level. One thing if I started nowadays I would do is use basic columns (x coordinates) to add '|' inbetween the outputs(including detecting empty span positions), keep items with similarish y coordinates together on lines, and put it into ChatGPT to format as desired, I suspect this would avoid misreading.

I would say PaddleOCR is good in general for tables - it's much better (in terms of recall rate) at recognising numerical digits / symbols than Tesseract although I notice it often misrecognises "l" in "Lullaby/ml/million" etc as "1" sometimes.

The cloud providers have better table extraction iff you can guarantee the same format each time for the document.

A wide variety of PDFs (both in length and content) that can have a variety of different tables, real estate related with a lot of financial content. And I need to be able to run on local models / software (no parsing as a service, no OpenAI, etc).

Here's just one example: https://www.totalflood.com/samples/residential.pdf (I struggle getting accurate data out of the Sales Comp section - basically all approaches mix up the properties.

Sorry, this will be very hard to do. You can't really try and segment images based on lines as the tables probably varied. The floor plans and things... this data is very very challenging.

I would suggest your best bet is waiting 2 years for the next version of LLAVA to come out which may have capabilities to interpret very accurately on device. The progress with LLAVA has been fast recently but for now it's still a bit too inaccurate.