An open, documented workflow for layout segmentation and Automatic Text Recognition of mid-twentieth-century typescripts from the Prefecture of Naples for TEI encoding and named-entity extraction is presented.
This paper presents an end-to-end, IIIF-based pipeline that turns the digitised Armenian press of France into an interactive map of the 20th-century Parisian Armenian commercial community. On each page, commercial advertisements are located, read, and parsed into structured records, which are then geocoded and placed on the map. Western Armenian is under-resourced and unsupported by off-the-shelf layout and OCR models, so the pipeline uses vision-language models (VLMs) as a data-bootstrapping strategy: they produce usable structured records at a scale hand annotation could not reach, and stay reliable on the strongly curved scans where conventional line-level CRNN OCR breaks down. The contribution includes a 500-page Western Armenian press corpus with 3,270 advertisement-level annotations, a Label Studio template that captures detection and semantic fields in a single annotation pass, and a reproducible workflow transposable to other under-resourced historical corpora. More broadly, the work shows that VLM-driven data bootstrapping is an effective lever for under-resourced historical languages such as (Western) Armenian.
Chahan Vidal-Gorène, Seda Kirakosyan, Edita Matevosyan· 0 citations
Released in 2025, Institutional Books: Harvard Library (IB-HL) is a collection of 983,004 volumes (242B o200k_base tokens), originally digitized through Harvard Library's participation in the Google Books Library project. As researchers and developers have begun to use IB-HL, a tension has emerged between standard large-scale preprocessing practices and the goals of careful information stewardship. Many existing pipelines optimize for web text: as a result, they tend to aggressively filter, deduplicate, restrict by language, and sometimes discard meaningful metadata. Meanwhile, researchers seeking to use IB-HL duplicate effort while performing similar processing and analysis. We describe an approach that we call Enriched Text. Instead of producing a single'complete'stream of tokens, we normalize the text while preserving metadata through annotations. We separate endmatter, detect per-paragraph language, identify clusters of duplicate paragraphs, and compute per-paragraph bits-per-byte scores. We provide this information through HTML-like annotations layered on top of the text. By parsing these annotations, users can tailor the output to their own needs instead of accepting a global editorial decision on content. The pipeline applies to all $\approx$250 languages in the collection. This report describes this project's goals, implementation, and design rationale. The release includes IB-HL-ET (an enriched-text version of IB-HL containing 217B o200k_base tokens across 983,003 volumes, organized into 1.39B annotated subtopic paragraphs) and the pipeline that produced it. These serve to make the collection easier for machines to parse and for humans to study.
David Lowry-Duda, Matteo Cargnelutti, Catherine Brobston et al.· 0 citations
An end-to-end approach for automated historical postcard indexing that integrates computer vision and natural language processing techniques is presented and effective integration of multiple AI techniques for automated heritage document analysis is demonstrated.
Matthieu Pélingre, Salvatore Tabbone· Journal on Computing and Cul...· 0 citations
A methodologically sound, AI-assisted framework for unlocking complex, multimodal historical archives is proposed by proposing a methodologically sound, AI-assisted framework for unlocking complex, multimodal historical archives.
This work presents an end-to-end archival processing and retrieval framework that integrates large language models (LLMs) into the archival pipeline and demonstrates that integrating LLMs with established document processing and retrieval pipelines can elevate digital libraries from static repositories to interactive, semantically searchable archival systems.
Stergios Konstantinidis, Hayman Lotfy, Alexis Erne et al.· 0 citations
This paper presents the methodological workflow developed for HOLYLAB-DB, a digital database based on the account extracts sent by the commissariats of the Franciscan Custody of the Holy Land to Rome between 1654 and 1750, and offers a transparent model for the curation of complex historical datasets.
Antonio Iodice, Alessia De Benedictis· 0 citations