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.
The digitisation of historical, administrative, and personal documents in Bulgarian faces considerable challenges due to the lack of robust Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) systems tailored for the Cyrillic alphabet. While modern Vision-Language Models (VLMs) and large transformer-based architectures achieve state-ofthe-art results, their performance and resource efficiency on low-resource languages remain prohibitive for decentralised, privacy-preserving applications. In this paper, we present a comprehensive, resource-efficient pipeline for extracting printed and handwritten Cyrillic text. Our system integrates advanced image preprocessing, YOLO-based document structure and table recognition, and a Permuted Autoregressive Sequence (PARSeq) model trained specifically for Bulgarian. We generated custom synthetic Bulgarian cursive datasets to mitigate the severe lack of real-world training data. Our evaluation indicates that the specialised PARSeq model outperforms traditional OCR tools such as Tesseract and EasyOCR on our custom degraded printed test set, and provides a practical, resource-efficient baseline for handwriting recognition compared to a modern local VLM (Qwen3-VL-4B). Finally, we discuss the discrepancy between synthetic and real handwritten data, highlighting the urgent need for a standardised, annotated Bulgarian HTR dataset.
D. Halachev, Ivan Koychev· Computational Linguistics in...· 0 citations
Many historical handwritten records in low-resource languages remain difficult to access through modern digital systems. This limits efforts to preserve and study cultural heritage at scale. Chagatai manuscripts exemplify these challenges within the Eastern Turki tradition. For centuries, it served as a major written language across Central Asia and supported a rich literary tradition. Large collections of Chagatai manuscripts still survive today, yet only a small amount of this material exists in digital form. As the technical literature specifically focused on Chagatai-HTR remains in its nascent stage, this review synthesizes indirect evidence from taxonomically related Perso-Arabic scripts to establish a foundational research framework. This article presents 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. Analyzing 50 studies published between 2020 and 2026, the review categorizes research trends into handwritten text recognition (HTR), optical character recognition (OCR), script classification, dataset development, and multimodal vision–language systems. The findings reveal a significant architectural shift from traditional segmentation-based CNN and RNN models toward transformer architectures and multimodal approaches. However, for Chagatai specifically, the primary obstacle is not the lack of advanced models but a critical scarcity of basic research infrastructure, including expert-verified transcriptions, annotation standards, and open benchmark datasets. Consequently, this article proposes a concrete development roadmap focusing on systematic digitization, expert annotation, transfer learning, and the creation of baseline models to enable reproducible evaluations.
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 collections.
Bilal Abdulrahman, Farhan Mohamed· Journal of Human Centered Te...· 0 citations