https://github.com/mqudsi/epstein-ocr
https://github.com/mqudsi/epstein-ocr
base64 cnn epstein ml ocr pytorch rust
Last synced: 27 days ago
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- Host: GitHub
- URL: https://github.com/mqudsi/epstein-ocr
- Owner: mqudsi
- Created: 2026-02-07T01:07:14.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2026-03-29T17:07:19.000Z (3 months ago)
- Last Synced: 2026-05-24T00:33:47.491Z (27 days ago)
- Topics: base64, cnn, epstein, ml, ocr, pytorch, rust
- Language: Python
- Homepage: https://neosmart.net/blog/efta00400459-has-been-cracked-dbc12-pdf-liberated/
- Size: 1020 KB
- Stars: 132
- Watchers: 5
- Forks: 14
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
# Project Summary
If you're new here, this project was started in response to [an effort to extract some unredacted content in the Epstein archives](https://neosmart.net/blog/recreating-epstein-pdfs-from-raw-encoded-attachments/). The CNN in this repo was used to successfully exfiltrate [DBC12.pdf](https://archive.org/details/dbc-12-one-page-invite-with-reply) from [EFTA00400459](https://archive.org/details/efta-00400459_pages).
You can read about this code/approach here: [EFTA00400459 has been cracked, DBC12.pdf liberated](https://neosmart.net/blog/efta00400459-has-been-cracked-dbc12-pdf-liberated/).
The code in this project runs against the images extracted from the PDF with `pdfimages`, you can download [an archive containing them here](https://archive.org/details/efta-00400459_pages).
### Basic Usage Info
Expects `../EFTA00400459-{000..=075}_2x.png` to exist
* Run `./train.sh` to generate training from train_top.txt and train_bot.txt corresponding to page-001_2x.png
* Run `./run.sh` to OCR all pages and generated recovered.pdf
Trains from top of page-001 and bottom of page-001 non-contiguously to capture vertical drift.
Memorizes grid location and reuses for subsequent pages (non-training runs) to prevent pixel shifts.
In training runs with `-d`/`--debug`, generates a debug view that lets you see if you mis-typed anything by showing greatest outliers compared to the rest of the members assigned to the bucket:
In inference runs, generates a debug view (when `-d` is in use with no `-q`/`--quiet`) that shows the max outliers compared to the rest of the characters in the image. When `-o`/`--output` is specified, the debug view is saved to `-proof.png` so you can inspect it later.
