README.md

metadata

license: mit
library_name: onnx
tags:
  - onnx
  - pdf
  - pdf-repair
  - tabular-classification
  - scikit-learn
datasets:
  - utilvo-com/pdf-corruption-lab
pipeline_tag: tabular-classification
metrics:
  - accuracy
model-index:
  - name: pdf-damage-classifier
    results:
      - task:
          type: tabular-classification
          name: PDF damage type classification
        dataset:
          type: utilvo-com/pdf-corruption-lab
          name: PDF Corruption Lab (synthetic, 25% stratified hold-out)
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy (synthetic hold-out, 300 files)

pdf-damage-classifier

A tiny (75 KB) ONNX model that looks at the raw bytes of a PDF and predicts what kind of structural damage it has, so a repair tool can choose the right recovery strategy. It does not need a PDF parser, so it works on files that are too broken to open.

Trained from scratch (scikit-learn RandomForestClassifier, 60 trees, depth 8, exported with skl2onnx) on the synthetic utilvo-com/pdf-corruption-lab dataset. No third-party models or documents are used.

Classes

valid, missing_eof, invalid_startxref, scrambled_xref, bad_root_reference, truncated

Class	Typical repair strategy
valid	nothing to do
missing_eof	append the %%EOF marker
invalid_startxref	locate the xref table and rewrite startxref
scrambled_xref	rebuild the xref table by scanning for N G obj
bad_root_reference	find the /Type /Catalog object and fix the trailer
truncated	scan surviving objects and rebuild page tree, xref and trailer

Input

A float32 vector of 12 structural features computed by pdf_features.py. The order is defined in labels.json.

Feature	Meaning
has_pdf_header	file starts with %PDF-
eof_in_last_1k / ends_with_eof	%%EOF marker near / at the end
startxref_present / startxref_numeric	startxref exists and is followed by a number
startxref_points_to_xref	that number is an offset where xref really starts
xref_valid_fraction	share of xref entries that point to a real N G obj
has_trailer, root_object_exists	trailer present; /Root target object exists
obj_endobj_gap, stream_endstream_gap	relative imbalance of obj/endobj and stream/endstream
log10_size	log10 of file size in bytes

Usage

pip install -r requirements.txt
python predict.py broken.pdf
# broken.pdf: scrambled_xref (p=0.99)

Or directly with onnxruntime:

import json, numpy as np, onnxruntime as ort
from pdf_features import extract_features

meta = json.load(open("labels.json"))
sess = ort.InferenceSession("pdf_damage_classifier.onnx")
x = np.array([extract_features(open("broken.pdf", "rb").read())], dtype=np.float32)
label_idx, probs = sess.run(None, {sess.get_inputs()[0].name: x})
print(meta["classes"][int(label_idx[0])])

Evaluation and limitations (please read)

• Accuracy on the 25 % stratified hold-out split (300 synthetic files) is 100 %, and the 7 reference files in output/ of the dataset repo are all classified correctly.
• This number is not evidence of real-world accuracy. Damage types are generated by simple rules and are largely separable by the features, so the task is easy by construction. Training and test files come from the same generator.
• Training files are produced by pdf-lib with classic xref tables (no object streams / xref streams) and are small. PDFs from other software, encrypted files, incremental updates, and real-world corruption (bit flips, partial overwrites, mixed damage) are not covered and will likely be misclassified.
• Use it as a quick triage hint, not as a validator.

Reproduce

git clone https://huggingface.co/datasets/utilvo-com/pdf-corruption-lab
python train.py pdf-corruption-lab/training .

Intended use

Triage for PDF repair pipelines, teaching material for ONNX export of scikit-learn models, and as a baseline to beat with better data.

Maintainers

Maintained by the Utilvo team.

License

MIT.