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DeepSeek OCR

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990 points pierre | 1 comments | 20 Oct 25 06:26 UTC | HN request time: 0s | source
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pietz ◴[20 Oct 25 08:49 UTC] No.45641449[source]▶
My impression is that OCR is basically solved at this point.

The OmniAI benchmark that's also referenced here wasn't updated with new models since February 2025. I assume that's because general purpose LLMs have gotten better at OCR than their own OCR product.

I've been able to solve a broad range of OCR tasks by simply sending each page as an image to Gemini 2.5 Flash Lite and asking it nicely to extract the content in Markdown under some additional formatting instructions. That will cost you around $0.20 for 1000 pages in batch mode and the results have been great.

I'd be interested to hear where OCR still struggles today.

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1. cle ◴[20 Oct 25 15:27 UTC] No.45645032[source]▶
That will not work with many of the world's most important documents because of information density. For example, dense tables or tables with lots of row/col spans, or complex forms with checkboxess, complex real-world formatting and features like strikethroughs, etc.

To solve this generally you need to chunk not by page, but by semantic chunks that don't exceed the information density threshold of the model, given the task.

This is not a trivial problem at all. And sometimes there is no naive way to chunk documents so that every element can fit within the information density limit. A really simple example is a table that spans hundreds pages. Solving that generally is an open problem.