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Archive-Scale Infrastructure Plan-Set Data Extraction

Transforming Historical Engineering Archives into Structured, Searchable Assets with Field-Level Confidence Scoring

A leading US infrastructure firm needed to convert over a century of bridge plan sets into a structured database. By deploying a specialized computer vision pipeline, they automated archive-scale data extraction across 4,000+ bridges with field-level precision. 

Challenges

  • Unstructured Historical Archives: Managing over 4,000 bridges and 8,000+ plan-set PDFs dating back to 1890, often running up to 390 pages per set with no extractable text layer.
  • Inconsistent Reading Order & CAD Layers: Modern CAD sheets (2015+) failed standard OCR/text layer extraction due to non-sequential reading order of dimension text. 
  • Dangerous AI Hallucination: Standard LLMs extracted figures with false confidence (e.g., misreading hand-drawn dimensions like 5′-8″ without signaling error), creating safety risks.

The Solution

Deployment of a specialized computer vision pipeline engineered to treat every sheet as a visual drawing, incorporating intelligent page triage and field-level uncertainty flags.

  • Intelligent Page Triage: Automatically locates the single general-plan sheet within a 300+ page plan-set, drastically lowering compute costs.
  • Field-Level Confidence Scoring: Evaluates individual schema fields such as construction date, span lengths, skew, and deck width independently rather than assigning a single document score.
  • Audit Provenance & Tracking: Records exact source PDF and page number references for every extracted value for rapid human verification.
  • Continuous Review Loop: Low-confidence or unreadable fields trigger human review, feeding corrections directly into subsequent processing batches.

Business Outcomes & Impact

  • Eliminated Hallucination Risk: Reliably outputs review flags for low-confidence data and non-applicable markers for mismatched fields rather than guessing.
  • Massive Efficiency Gains: Replaced full-manual document audits with targeted human-in-the-loop review focused only on uncertain fields.
  • High-Fidelity Archive Extraction: Successfully processed multi-era schematics spanning over a century of drafting standards (1890s–present).
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