Digital Approaches to Handwritten Text Recognition in Complex Art-Historical Archives: Towards a Robust Text Model for the Giovanni Battista Cavalcaselle Manuscripts
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 paper presents a comprehensive, resource-efficient pipeline for extracting printed and handwritten Cyrillic text that integrates advanced image preprocessing, YOLO-based document structure and table recognition, and a Permuted Autoregressive Sequence (PARSeq) model trained specifically for Bulgarian.
D. Halachev, Ivan Koychev· Computational Linguistics in...· 0 citations
A systematic literature review following the PRISMA guidelines to examine artificial intelligence methods for handwritten text recognition (HTR) and text restoration in low-resource languages and proposes a concrete development roadmap focusing on systematic digitization, expert annotation, transfer learning, and the c...
Zhanibek Balabayev, S. Biloshchytska, Beibit Abdikenov et al.· Information· 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.
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
An open-source end-to-end pipeline for detecting, classifying, deduplicating, and captioning visual elements from historical book collections is introduced and an initial dataset of 22.6 million visual elements extracted from the Institutional Books: Harvard Library dataset is released.
Jimmy Mendez, Matteo Cargnelutti, David Lowry-Duda et al.· 0 citations
An edge-aware line-level HTR framework that extends a CNN-Transformer baseline with a learnable edge-extraction channel and Squeeze-and-Excitation channel attention and shows that combining learnable structural cues with channel-wise attention has improved robustness for degradation-prone historical manuscript collecti...
Bilal Abdulrahman, Farhan Mohamed· Journal of Human Centered Te...· 0 citations
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