Aug 2026· Journal on Computing and Cultural Heritage· 0 citations· 53 references
TL;DR
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.
Abstract
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.
The novel task of cross-modal query suggestion is introduced, which interactively guides users by suggesting textual refinements based on visual clusters identified in the retrieval results, and the creation of CroQS, a benchmark dataset comprising 50 diverse queries and 295 semantic clusters in generic domain.
Giacomo Pacini, Nicola Messina, Nicola Tonellotto et al.· 0 citations
Extracting structured information from visually rich documents remains a complex task due to variations in layout, text alignment, and reading order. Traditional methods based on IOB tagging or graph decoding often struggle with irregular text sequences and the computational burden of large relational graphs. This paper introduces a novel anchor-based approach that redefines entity representation and association for structured information extraction. The proposed model, named Hwte, integrates visual and linguistic features through a multi-modal transformer architecture that jointly detects entities and their relationships. A new pre-training objective, Masked Detection Modelling (MDM), is introduced to enhance the model’s ability to predict both textual and spatial information simultaneously. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves superior accuracy and robustness compared to existing solutions, highlighting its effectiveness for real-world document understanding tasks.
Anbu Lakshmi S, P. R. Raksha, Mohamadi Ghouisya Kousar et al.· International Research Journ...· 0 citations
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
The Institutional Newspapers Pipeline is presented, a modular system designed to extract high-quality, structured datasets from historical newspaper scans that was architected so that each step remains interpretable and customizable, and so that the pipeline as a whole remains computationally frugal enough to run on workstation-level hardware.
Matteo Cargnelutti, Catherine Brobston, Eben English et al.· 0 citations
This article proposes a pragmatic workflow for accelerating access to and analysis of large Arabic manuscript collections. Faced with the immense volume of historical texts and the inherent challenges of traditional approaches to hand-written text recognition (HTR), the proposed method combines off-the-shelf, cloud-based Optical Character Recognition (OCR) with large language models (LLMs). Despite the imperfect output often produced by Arabic HTR, this research demonstrates that LLMs can effectively derive meaningful summaries and key insights, enabling rapid triage for scholars. The workflow significantly enhances research efficiency by allowing for the swift identification of valuable documents, thereby freeing human expertise for in-depth analysis. While this approach holds particular importance for endangered archives, such as those in the Sudan, the paper points to its broader potential: manuscript scholars mastering AI tools not to replace but to support and enhance human understanding.
Keywords: Arabic manuscripts • Handwritten text recognition (HTR) • Artificial intelligence • Digital humanities • Endangered archives, Sudan
Albrecht Hofheinz· Journal of Arabic and Islami...· 0 citations
