# Quid est VERITAS? A Modular Framework for Archival Document Analysis

arXiv: https://arxiv.org/abs/2603.28108 · HTML: https://mlsanity.com/paper/2603.28108/

## 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.

## Builds on (most cited first, 7 of 7)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks](https://mlsanity.com/paper/2012.14163/) (2020)
- [OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches](https://mlsanity.com/paper/2502.01205/) (2025)
- [Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records](https://mlsanity.com/paper/2501.11623/) (2025)
- [CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition](https://mlsanity.com/paper/2509.19768/) (2025)
- [Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities](https://mlsanity.com/paper/2510.06743/) (2025)
- [The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses](https://mlsanity.com/paper/2404.18706/) (2024)

## 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The digitisation of historical documents has traditionally been conceived as a process limited to character-level transcription, producing flat text that lacks the structural and semantic information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.