AcroMELD
πͺ Turns a flat PDF into a fillable form. Give it a PDF that looks like a form but has no form fields β it finds every field and writes a real, clickable AcroForm.
pip install acromeld
acromeld input.pdf output.pdf
That is all you need; the weights below are downloaded on first use.
*AcroMELD = AcroForm Multi-source Evidence Linking Decoder.*
What it detects
Three field types β Text, Choice, Signature β plus a learned link that merges several visual candidates into one field instead of emitting duplicates. It reads two channels at once: the rendered page, and the PDF's own drawing primitives (lines, rectangles, glyph runs), which a purely visual detector ignores. Scanned pages have no primitives; the model was trained with that channel dropped on 12 % of pages, so it degrades rather than fails.
39.4M parameters. Up to 896 fields per page, so dense forms are not silently truncated.
How well it works
Measured once on a sealed holdout of 1,996 PDFs / 6,843 pages the model never saw during training, against a pass/fail threshold registered before training started.
| containment micro-F1 | |
|---|---|
| Registered baseline β the number to beat | 0.82903655889853 |
| AcroMELD | 0.8476748634830094 |
| Verdict | passed |
The model was frozen and hash-locked before the holdout was opened, so the score could not be tuned after the fact.
Read this before using it:
- Signature detection does not work. At the calibrated threshold the model predicts
essentially no signature fields β per-class F1
0.0677. Text and Choice carry the entire score. Treat any signature output as unusable. - Under a stricter IoU/COCO adapter the same model reaches only
0.28996mAP, below a locally evaluated CommonForms-L reference. The two adapters use different ground-truth counts and are not comparable to each other; the strict number is the less flattering one and it is reported here for that reason. - Measured on German-language forms. Other languages and layouts are untested.
- Rotated pages are rejected rather than silently misplaced.
- One sealed run, one seed. No stability claim, no component ablations.
This is a research artifact, not a product.
Files
acromeld-inference.pt |
158 MB | the model β EMA weights, what the acromeld package loads |
operating-point.json |
199 B | the calibrated thresholds, frozen before the holdout was opened |
{
"class_thresholds": [0.769, 0.812, 0.99],
"link_probability": 0.95,
"nms_iou": 1.0
}
Class order is [Text, Choice, Signature]. nms_iou: 1.0 means suppression is effectively
disabled β the graph-set decoder produces exclusive queries, so duplicate suppression is not
needed.
Architecture
384 ECDet-L visual queries + 384 structure-seeded queries + 128 free recovery queries β 896 exclusive queries β 4 sparse geometry-weighted graph layers β boxes, 3 classes + no-object, localization quality, same-field links.
The visual branch is vendored ECDet-L (EdgeCrafter, Apache-2.0). The label-free PDF-structure encoder and the graph-set decoder are this project's contribution.
Trained on 35,388 PDFs / 119,418 pages, 2Γ NVIDIA RTX A6000, effective batch 32, epoch 32 of 33 selected on a development split by the same containment metric the gate uses. The external holdout was excluded from the training index by document hash.
What is not here
- The training corpus. Third-party form documents with heterogeneous redistribution rights and potentially sensitive content; releasing it would require a rights and data-protection assessment that has not been done.
- The training code. The objective, the Hungarian matching, the calibration procedure and the evaluation harness are not published.
- The sealed run record β the frozen candidate checkpoint, the full per-epoch history, the one-shot holdout report and every provenance digest that binds them. Held privately and available for hash audit on request.
Licence
Apache-2.0, inherited from the source project and from vendored ECDet-L.
The two example pages above are blank third-party form templates, rendered from the model's own output and shown only to illustrate what it does. They carry their publishers' rights.

