Yeah that’s why heuristics should work on the lowest possible layer, not on pdftotext. If you use pdftotext you’re stripping positional data and other stuff.
Do you use a public set of documents? I bet I could almost oneshot this with my harness :p
Do you use a public set of documents? I bet I could almost oneshot this with my harness :p
Here’s a public appendix from a recent Federal Circuit case. It has a representative assortment of documents (opinions, briefs, patents, transcripts) but contains only cited pages to the appendix pagination is non-consecutive: https://www.courtlistener.com/docket/68048163/15/ecofactor-i...
The underlying docket is usually 100 to 1,000 times larger than this but it’s similar types of files and the appendix shows the various types of headers and footers that can exist.
https://kolinko.eu/pdf-reading-order/
But I wonder about your opinion.
> The EcoFactor result inverts. There, on hand-authored gold over 21 pages of clean native text, pdfgeo won the accuracy checks 25/42 to 19/42 — its geometry work on multi-column patents, table cells and timestamp lanes is genuinely better, and nothing here contradicts that. But this corpus is 21,652 pages of mostly scanned, OCR'd, skewed exhibits, and at that scale pdfgeo's robustness is the binding constraint: it loses 6.4% of the corpus to two trivial crashes and an over-strict rotation test, duplicates text on 34 files, and drops 80% of wrap hyphens. lawpdf extracted every page, 7× faster, with 2.5× less furniture leakage.
It looks like the actual geometric analysis came out the same general shape, which is an ad hoc heuristic with hard-coded values attempting to figure out where the gutter is located, etc. Seems like both have some overfitting to the text data set.
Thanks for the benchmark, I was seriously looking forward to it! Would you consider such even results good for a one-shot? I wonder how well my harness performs :)