Comment by kbumsik
3 months ago
> My impression is that OCR is basically solved at this point.
Not really in practice to me. Especially they still struggle with Table format detection.
3 months ago
> My impression is that OCR is basically solved at this point.
Not really in practice to me. Especially they still struggle with Table format detection.
This.
Any complex parent table span cell relationship still has low accuracy.
Try the reverse, take a complex picture table and ask Chatgpt5, claude Opus 3.1, Gemini Pro 2.5 to produce a HTML table.
They will fail.
Maybe I misunderstood the assignment but it seems to work for me.
https://chatgpt.com/share/68f5f9ba-d448-8005-86d2-c3fbae028b...
Edit: Just caught a mistake, transcribed one of the prices incorrectly.
Right, I wouldn't use full table detection to VLM model because they tend to mistake with numbers in table...
Maybe my imagination is limited or our documents aren't complex enough, but are we talking about realistic written documents? I'm sure you can take a screenshot of a very complex spreadsheet and it fails, but in that case you already have the data in structured form anyway, no?
> realistic written documents?
Just get a DEF 14A (Annual meeting) filing of a company from SEC EDGAR.
I have seen so many mistakes when looking at the result closely.
Here is a DEF 14A filing from Salseforce. You can print it to a PDF and then try converting.
https://www.sec.gov/Archives/edgar/data/1108524/000110852425...
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Now if someone mails or faxes you that spreadsheet? You're screwed.
Spreadsheets are not the biggest problem though, as they have a reliable 2-dimensional grid - at worst some cells will be combined. The form layouts and n-dimensional table structures you can find on medical and insurance documents are truly unhinged. I've seen documents that I struggled to interpret.
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I had mentioned this when the new QWEN model dropped - I have a stack of construction invoices that fail through both OCR and OpenAI.
It's a hard (and very interesting) problem space.