mlsanityData to 14 Oct 2026

Quid est VERITAS? A Modular Framework for Archival Document Analysis

arXiv 2603.28108 ↗First posted 30 Mar 2026cs.DL cs.AI cs.IRcited by 0

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Abstract (arXiv)

The digitisation of historical documents has traditionally been conceived as a process limited to character-level transcription, producing flat text that lacks the structural and semantic information necessary for substantive computational analysis. We present VERITAS (Vision-Enhanced Reading, Interpretation, and Transcription of Archival Sources), a modular, model-agnostic framework that reconceptualises digitisation as an integrated workflow encompassing transcription, layout analysis, and semantic enrichment. The pipeline is organised into four stages - Preprocessing, Extraction, Refinement, and Enrichment - and employs a schema-driven architecture that allows researchers to declaratively specify their extraction objectives. We evaluate VERITAS on the critical edition of Bernardino Corio's Storia di Milano, a Renaissance chronicle of over 1,600 pages. Results demonstrate that the pipeline achieves a 67.6% relative reduction in word error rate compared to a commercial OCR baseline, with a threefold reduction in end-to-end processing time when accounting for manual correction. We further illustrate the downstream utility of the pipeline's output by querying the transcribed corpus through a retrieval-augmented generation system, demonstrating its capacity to support historical inquiry.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 30 Mar 2026.

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References · 7

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2020Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networkscited by 11
  3. 2025OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunchescited by 11
  4. 2025Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical recordscited by 10
  5. 2025CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognitioncited by 9
  6. 2025Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanitiescited by 5
  7. 2024The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censusescited by 4

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  1. 2026When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents
  2. 2026Improving Access to Historical Archives with Real-time RAG-based Systems ↗
  3. 2026Structure-Aware Text Recognition for Ancient Greek Critical Editions
  4. 2026When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts
  5. 2026PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing
  6. 2026A Survey of OCR Evaluation Methods and Metrics and the Invisibility of Historical Documents
  7. 2026Error Patterns in Historical OCR: A Comparative Analysis of TrOCR and a Vision-Language Model
  8. 2026From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction