From OCR to Analysis: Tracking Correction Provenance in Digital Humanities Pipelines

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Social Sciences & Behavioral Studies, Research Methodology & Innovation · Depth: Advanced, quick

Summary

A provenance-aware framework is introduced to track correction lineage in OCR-corrected digital humanities corpora. This framework records details like edit type, correction source, confidence, and revision status at the span level, addressing the issue of common workflows overwriting intermediate OCR correction decisions. A pilot corpus of historical texts was used to compare named entity extraction across raw OCR, fully corrected text, and provenance-filtered corrections. The study found that different correction pathways can substantially alter extracted entities and document-level interpretations. The framework's provenance signals are shown to help identify unstable outputs and prioritize human review, supporting reproducibility, source criticism, and uncertainty-aware interpretation in NLP for digital humanities.

Key takeaway

For NLP Engineers building digital humanities text pipelines, you should integrate provenance tracking into your OCR correction workflows. This approach, recording edit type, source, and confidence at the span level, will enable you to identify unstable outputs and prioritize human review effectively. Implementing this framework supports robust reproducibility and uncertainty-aware interpretation, preventing hidden textual transformations from skewing your downstream analytical results.

Key insights

Tracking OCR correction provenance at the span level is crucial for understanding its impact on downstream NLP tasks and scholarly interpretation.

Principles

• Provenance is a first-class analytical layer.
• Correction pathways alter extracted entities.
• Provenance signals identify unstable outputs.

Method

The framework records OCR correction lineage at the span level, detailing edit type, correction source, confidence, and revision status to preserve transformation history for analysis.

In practice

• Prioritize human review with provenance signals.
• Support reproducibility in NLP pipelines.
• Enhance source criticism with correction history.

Topics

• Optical Character Recognition
• Digital Humanities
• NLP Pipelines
• Provenance Tracking
• Named Entity Extraction
• Text Correction

Best for: Research Scientist, NLP Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.