Re the OCR, I'm currently running allenai/olmocr-2-7b against all the PDFs with text in them, comparing with the OCR DOJ provided, and a lot it doesn't match, and surprisingly olmocr-2-7b is quite good at this. However, after extracing the pages from the PDFs, I'm currently sitting on ~500K images to OCR, so this is currently taking quite a while to run through.
A case study in PDF forensics: The Epstein PDFs
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Re: A case study in PDF forensics: The Epstein PDFs
#12> Information leakage may also be occurring via PDF comments or orphaned objects inside compressed object streams, as I discovered above. hopefully someone is independently archiving all documents my understanding is that some are being removed
Initially under "Epstein Files Transparency Act (H.R.4405)" on https://www.justice.gov/epstein/doj-disclosures, all datasets had .zip links. I first saw that page when all but dataset 11 (or 10) had a .zip link. At one point this morning, all the .zip links were removed, now it seems like most are back again.
Re: A case study in PDF forensics: The Epstein PDFs
#13(2025) just follow hn guideline, impressive voter ring though
We're in early February 2025 [edit:2026] and the article was written on Dec 23, 2025, which makes it less than two months old. I think it's ok not to include a year in the submission title in that case. I personally understand a year in the submission as a warning that the article may not be up to date.
Less about the age, and more about confusing what they are analyzing, for the files that were just released like a week ago.
Re: A case study in PDF forensics: The Epstein PDFs
#14> DoJ explicitly avoids JPEG images in the PDFs probably because they appreciate that JPEGs often contain identifiable information, such as EXIF, IPTC, or XMP metadata
Maybe I'm underestimating the issue at full, but isn't this a very lightweight problem to solve? Is converting the images to lower DPI formats/versions really any easier than just stripping the metadata? Surely the DOJ and similar justice agencies have been aware of and doing this for decades at this point, right?
Re: A case study in PDF forensics: The Epstein PDFs
#15Any guesses why some of the newest files seem to have random ”=” characters in the text? My first thought was OCR, but it seemed to not be linked to characters like ”E” that could be mistakenly interpreted by an OCR tool. My second guess is just making it more difficult to produce reliable text searches, but probably 90% of HN readers could find a way to make a search tool that does not fall apart in case a ”=” character is found (although making this work for long search queries would make the search slower).
Re: A case study in PDF forensics: The Epstein PDFs
#16Any guesses why some of the newest files seem to have random ”=” characters in the text? My first thought was OCR, but it seemed to not be linked to characters like ”E” that could be mistakenly interpreted by an OCR tool. My second guess is just making it more difficult to produce reliable text searches, but probably 90% of HN readers could find a way to make a search tool that does not fall apart in case a ”=” chara…
Was on the frontpage yesterday: https://news.ycombinator.com/item?id=46868759
Re: A case study in PDF forensics: The Epstein PDFs
#17Re the OCR, I'm currently running allenai/olmocr-2-7b against all the PDFs with text in them, comparing with the OCR DOJ provided, and a lot it doesn't match, and surprisingly olmocr-2-7b is quite good at this. However, after extracing the pages from the PDFs, I'm currently sitting on ~500K images to OCR, so this is currently taking quite a while to run through.
Did you take any steps to decrease the dimension size of images, if this increases the performance? I have not tried this as I have not peformed an OCR task like this with an LLM. I would be interested to know at what size the vlm cannot make out the details in text reliably.
Re: A case study in PDF forensics: The Epstein PDFs
#18Re the OCR, I'm currently running allenai/olmocr-2-7b against all the PDFs with text in them, comparing with the OCR DOJ provided, and a lot it doesn't match, and surprisingly olmocr-2-7b is quite good at this. However, after extracing the pages from the PDFs, I'm currently sitting on ~500K images to OCR, so this is currently taking quite a while to run through.
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Re: A case study in PDF forensics: The Epstein PDFs
#19Re the OCR, I'm currently running allenai/olmocr-2-7b against all the PDFs with text in them, comparing with the OCR DOJ provided, and a lot it doesn't match, and surprisingly olmocr-2-7b is quite good at this. However, after extracing the pages from the PDFs, I'm currently sitting on ~500K images to OCR, so this is currently taking quite a while to run through.
Did you take any steps to decrease the dimension size of images, if this increases the performance? I have not tried this as I have not peformed an OCR task like this with an LLM. I would be interested to know at what size the vlm cannot make out the details in text reliably.
The performance is OK, takes a couple of seconds at most on my GPU, just the amount of documents to get through that takes time, even with parallelism. The dimension seems fine as it is, as far as I can tell.
Re: A case study in PDF forensics: The Epstein PDFs
#20Re the OCR, I'm currently running allenai/olmocr-2-7b against all the PDFs with text in them, comparing with the OCR DOJ provided, and a lot it doesn't match, and surprisingly olmocr-2-7b is quite good at this. However, after extracing the pages from the PDFs, I'm currently sitting on ~500K images to OCR, so this is currently taking quite a while to run through.
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Haven't seen anything particular about that, but lots of the documents with names that were half-redacted contain OCRd text that is completely garbled, but olmocr-2-7b seems to handle it just fine. Unsure if they just had sucky processes or if there is something else going on.