mlsanityData to 14 Oct 2026

The Devil is in the Details -- From OCR for Old Church Slavonic to Purely Visual Stemma Reconstruction

arXiv 2604.11724 ↗First posted 13 Apr 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The age of artificial intelligence has brought many new possibilities and pitfalls in many fields and tasks. The devil is in the details, and those come to the fore when building new pipelines and executing small practical experiments. OCR and stemmatology are no exception. The current investigation starts comparing a range of OCR-systems, from classical over machine learning to LLMs, for roughly 6,000 characters of late handwritten church slavonic manuscripts from the 18th century. Focussing on basic letter correctness, more than 10 CS OCR-systems among which 2 LLMs (GPT5 and Gemini3-flash) are being compared. Then, post-processing via LLMs is assessed and finally, different agentic OCR architectures (specialized post-processing agents, an agentic pipeline and RAG) are tested. With new technology elaborated, experiments suggest, church slavonic CER for basic letters may reach as low as 2-3% but elaborated diacritics could still present a problem. How well OCR can prime stemmatology as a downstream task is the entry point to the second part of the article which introduces a new stemmatic method based solely on image processing. Here, a pipeline of automated visual glyph extraction, clustering and pairwise statistical comparison leading to a distance matrix and ultimately a stemma, is being presented and applied to two small corpora, one for the church slavonic Gospel of Mark from the 14th to 16th centuries, one for the Roman de la Rose in French from the 14th and 15th centuries. Basic functioning of the method can be demonstrated.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 13 Apr 2026.

Builds on

6 of 22 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014CNN Features off-the-shelf: an Astounding Baseline for Recognitioncited by 316
  3. 2025Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documentscited by 12
  4. 2025OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunchescited by 11
  5. 2025Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanitiescited by 5
  6. 2023Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAIcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Error Patterns in Historical OCR: A Comparative Analysis of TrOCR and a Vision-Language Model
  2. 2025Finetuning Vision-Language Models as OCR Systems for Low-Resource Languages: A Case Study of Manchu
  3. 2026When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts
  4. 2024OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?
  5. 2026A Survey of OCR Evaluation Methods and Metrics and the Invisibility of Historical Documents
  6. 2026Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study
  7. 2026When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study
  8. 2026OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios