AcroMELD

πŸͺ„ 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.

A PDF with no form fields on the left, the same page with 54 generated form fields on the right

*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.

41 detected fields on a form page, coloured by class with confidence scores

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 0.28996 mAP, 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.

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