Datasets:
Download gen_training.js from utilvo-com/pdf-corruption-lab: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/datasets/utilvo-com/pdf-corruption-lab/resolve/main/gen_training.js
- Command line
-
hf download hf://datasets/utilvo-com/pdf-corruption-lab/gen_training.js
-
curl -L -o gen_training.js https://huggingface.co/datasets/utilvo-com/pdf-corruption-lab/resolve/main/gen_training.js
3.12 kB
| /** | |
| * Generates a labelled training set of randomized valid/damaged PDFs | |
| * for the pdf-damage-classifier model. | |
| * | |
| * Usage: node gen_training.js [outputDir] [variantsPerClass] | |
| * Output: <outputDir>/*.pdf and <outputDir>/labels.csv (file,label) | |
| */ | |
| const fs = require('fs'); | |
| const path = require('path'); | |
| const { PDFDocument, rgb, StandardFonts } = require('pdf-lib'); | |
| const { scenarios, toLatin1, fromLatin1 } = require('./generate'); | |
| const IMG = 'iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAYAAABytg0kAAAAFElEQVQIW2NkwAJGDjACU2M4wAgAJu4BCHgB5hQAAAAASUVORK5CYII='; | |
| // Small seeded PRNG so the dataset is reproducible. | |
| function rng(seed) { | |
| let s = seed >>> 0; | |
| return () => { | |
| s = (s + 0x6d2b79f5) >>> 0; | |
| let t = s; | |
| t = Math.imul(t ^ (t >>> 15), t | 1); | |
| t ^= t + Math.imul(t ^ (t >>> 7), t | 61); | |
| return ((t ^ (t >>> 14)) >>> 0) / 4294967296; | |
| }; | |
| } | |
| const WORDS = 'alpha beta gamma delta report invoice summary table chart data page section total value note draft final review'.split(' '); | |
| async function randomPdf(r) { | |
| const doc = await PDFDocument.create(); | |
| const font = await doc.embedFont(r() < 0.5 ? StandardFonts.Helvetica : StandardFonts.TimesRoman); | |
| const pages = 1 + Math.floor(r() * 8); | |
| const png = r() < 0.6 ? await doc.embedPng(Buffer.from(IMG, 'base64')) : null; | |
| for (let p = 0; p < pages; p++) { | |
| const page = doc.addPage([r() < 0.8 ? 595 : 612, r() < 0.8 ? 842 : 792]); | |
| const lines = 1 + Math.floor(r() * 40); | |
| for (let i = 0; i < lines; i++) { | |
| const n = 3 + Math.floor(r() * 12); | |
| const text = Array.from({ length: n }, () => WORDS[Math.floor(r() * WORDS.length)]).join(' '); | |
| page.drawText(text, { x: 40, y: 800 - i * 14, size: 8 + Math.floor(r() * 5), font, color: rgb(r(), r(), r()) }); | |
| } | |
| if (png && r() < 0.5) page.drawImage(png, { x: 60, y: 100, width: 40 + r() * 100, height: 40 + r() * 100 }); | |
| } | |
| return doc.save({ useObjectStreams: false }); | |
| } | |
| async function main() { | |
| const outDir = path.resolve(process.argv[2] || path.join(__dirname, 'training')); | |
| const perClass = parseInt(process.argv[3] || '200', 10); | |
| fs.mkdirSync(outDir, { recursive: true }); | |
| const r = rng(42); | |
| const rows = ['file,label']; | |
| const classes = ['valid', ...scenarios.filter((s) => !s.id.startsWith('truncated')).map((s) => s.id), 'truncated']; | |
| for (const label of classes) { | |
| for (let i = 0; i < perClass; i++) { | |
| const base = await randomPdf(r); | |
| const text = toLatin1(base); | |
| let out; | |
| if (label === 'valid') out = base; | |
| else if (label === 'truncated') out = base.slice(0, Math.floor(base.length * (0.3 + r() * 0.6))); | |
| else out = scenarios.find((s) => s.id === label).apply(base, text); | |
| const name = `${label}_${String(i).padStart(4, '0')}.pdf`; | |
| fs.writeFileSync(path.join(outDir, name), out); | |
| rows.push(`${name},${label}`); | |
| } | |
| console.log(`${label}: ${perClass}`); | |
| } | |
| fs.writeFileSync(path.join(outDir, 'labels.csv'), rows.join('\n') + '\n'); | |
| console.log(`labels.csv written to ${outDir}`); | |
| } | |
| main().catch((e) => { | |
| console.error(e); | |
| process.exit(1); | |
| }); | |