{
  "id": 11087602,
  "title": "Ranking Scanned PDFs While Preserving OCR Uncertainty",
  "url": "https://urgent.news/2026/10/01/ranking-scanned-pdfs-while-preserving-ocr-uncertainty",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-01T02:31:10.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sourcebento/ranking-scanned-pdfs-while-preserving-ocr-uncertainty-2228"
  },
  "original_language": "en",
  "account": null,
  "summary": "The article discusses the challenges of ranking scanned PDFs while preserving OCR uncertainty. It explains that lexical and semantic retrieval are used to address the two separate search problems presented by scanned PDFs: finding the relevant passage and deciding how much to trust the text extracted from its pixels. The author emphasizes the importance of maintaining provenance for the reader to inspect doubtful text, which requires distinguishing between relevance, extraction quality, and human review. The article also highlights the importance of preserving evidence at the region boundary, such as a paragraph or table cell, to map back to the scan and provide useful fields for search, including the document version, extraction revision, physical page index, bounding box, OCR engine and configuration, raw confidence values, and assessment state.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}