| --- |
| license: mit |
| task_categories: |
| - tabular-classification |
| tags: |
| - pdf |
| - pdf-repair |
| - test-corpus |
| - synthetic |
| pretty_name: PDF Corruption Lab |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: features |
| data_files: features/train.csv |
| default: true |
| --- |
| |
| # pdf-corruption-lab |
|
|
| A **fully synthetic** corpus of damaged PDF files with a known kind and severity |
| of damage, the scripts that generate it, and a ready-to-use feature table. |
| It is meant for testing and benchmarking PDF repair / recovery tools and for |
| training small damage-type classifiers. |
|
|
| Everything here is original: documents are built from scratch with |
| [`pdf-lib`](https://github.com/Hopding/pdf-lib) (MIT). No third-party |
| documents or models are included. |
|
|
| ## What is in this repository |
|
|
| | Path | Description | |
| |---|---| |
| | `output/` | 7 hand-checkable reference files: one valid PDF and 6 damaged variants, plus `manifest.json` | |
| | `training/` | 1,200 randomized PDFs (200 per class: pages, fonts, text and images vary) and `labels.csv` | |
| | `features/train.csv` | 12 byte-level structural features + label for each training file (powers the dataset viewer) | |
| | `generate.js` | regenerates the reference files in `output/` | |
| | `gen_training.js` | regenerates the randomized training set (seeded, reproducible) | |
|
|
| ## Damage classes |
|
|
| | Class | Damage | Severity (1-5) | |
| |---|---|---| |
| | `valid` | none | 0 | |
| | `missing_eof` | trailing `%%EOF` removed | 1 | |
| | `invalid_startxref` | `startxref` value is non-numeric | 2 | |
| | `scrambled_xref` | xref offsets replaced with bogus values | 3 | |
| | `bad_root_reference` | trailer `/Root` points to a missing object | 3 | |
| | `truncated` | file cut at a random point (30-90 % of its length) | 4-5 | |
|
|
| ## Features (`features/train.csv`) |
|
|
| `has_pdf_header`, `eof_in_last_1k`, `ends_with_eof`, `startxref_present`, |
| `startxref_numeric`, `startxref_points_to_xref`, `xref_valid_fraction`, |
| `has_trailer`, `root_object_exists`, `obj_endobj_gap`, `stream_endstream_gap`, |
| `log10_size`, and `label`. The extraction code is in the model repo: |
| [`utilvo-com/pdf-damage-classifier`](https://huggingface.co/utilvo-com/pdf-damage-classifier). |
|
|
| ## Regenerate |
|
|
| ```bash |
| npm install |
| node generate.js ./output |
| node gen_training.js ./training 200 |
| ``` |
|
|
| ## Suggested evaluation for repair tools |
|
|
| For each damaged file, run your tool and record: |
|
|
| 1. Does the output open in a strict parser? |
| 2. How many pages were recovered? |
| 3. Was the embedded image preserved (where present)? |
|
|
| ## Limitations |
|
|
| All files are generated by `pdf-lib` with classic xref tables (no object streams |
| or xref streams) and each file has a single kind of damage. Real-world |
| corruption (bit flips, mixed damage, files from other producers, encryption) is |
| not represented. Treat results here as a controlled benchmark, not as a |
| guarantee for real files. |
|
|
| ## Disclosure |
|
|
| Maintained by the Utilvo team. The organization profile links to our website. |
|
|
| ## License |
|
|
| MIT. See `LICENSE`. |
|
|
