Beyond Archives: Designing a Segmentation and ATR Workflow for Mid-Twentieth-Century Italian Typescripts in a Relational Digital Library

Mapping Armenian Paris: Extracting and Geocoding Commercial Advertisements from the 20th-Century Diaspora Press

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.

Institutional Books - Enriched Text: A customizable multilingual open-source pipeline for denoising, deduplicating, and annotating OCR text at scale

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.

Automated Indexing of Historical Postcards: An End-to-End Approach Combining Image and Text Analysis

Historical postcards are of great interest to different researchers and especially historians. Preserving these documents for future generations is important. Indeed, they represent valuable cultural heritage documents combining visual imagery with textual information, but manual indexing of large digitized collections is prohibitively expensive in time and resources. This paper presents an end-to-end approach for automated historical postcard indexing that integrates computer vision and natural language processing techniques. Our modular pipeline employs fine-tuned YOLO models to detect text types and postal markings, EasyOCR for text transcription, CamemBERT for named entity recognition enabling automatic geolocation, and DINOv2 models for visual content classification across ten semantic topics. Support Vector Machines predict keywords for images lacking textual descriptions using DINOv2 image embeddings, while complex postal markings are segmented and transcribed. We demonstrate our approach on the recto (illustrated front side) of 4,293 digitized postcards from the Grand Est region of France (1899-1930). Our results show that, among the latest YOLO models, fine-tuned YOLOv12x achieves 0.83 weighted average precision for detection. Fine-tuned DINOv2 attains 0.91 weighted average precision for image classification, while CLIP and other classification models do not exceed 0.83. Finally, named entity recognition successfully geolocates 52% of postcards. In fully automatic operation, the complete pipeline assigns at least a coarse topic to almost every postcard and at least one richer field (a keyword, a geolocation, or a date) to about 80% of them, leaving fewer than 1% essentially un-indexed. Our work contributes to digital humanities by demonstrating effective integration of multiple AI techniques for automated heritage document analysis and provides a publicly available dataset for future research.

Digital Approaches to Handwritten Text Recognition in Complex Art-Historical Archives: Towards a Robust Text Model for the Giovanni Battista Cavalcaselle Manuscripts

Improving Access to Historical Archives with Real-time RAG-based Systems

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.

The Joys and Sorrows of Data Normalization: Working with Sources from the Franciscan Custody of the Holy Land, XVII–XVIII Centuries