TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912332978847744 |
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| author | Aguilar, Sergio Torres |
| author_facet | Aguilar, Sergio Torres |
| contents | This paper introduces TRIDIS (Tria Digita Scribunt), an open-source corpus of medieval and early modern manuscripts. TRIDIS aggregates multiple legacy collections (all published under open licenses) and incorporates large metadata descriptions. While prior publications referenced some portions of this corpus, here we provide a unified overview with a stronger focus on its constitution. We describe (i) the narrative, chronological, and editorial background of each major sub-corpus, (ii) its semi-diplomatic transcription rules (expansion, normalization, punctuation), (iii) a strategy for challenging out-of-domain test splits driven by outlier detection in a joint embedding space, and (iv) preliminary baseline experiments using TrOCR and MiniCPM2.5 comparing random and outlier-based test partitions. Overall, TRIDIS is designed to stimulate joint robust Handwritten Text Recognition (HTR) and Named Entity Recognition (NER) research across medieval and early modern textual heritage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER Aguilar, Sergio Torres Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Digital Libraries This paper introduces TRIDIS (Tria Digita Scribunt), an open-source corpus of medieval and early modern manuscripts. TRIDIS aggregates multiple legacy collections (all published under open licenses) and incorporates large metadata descriptions. While prior publications referenced some portions of this corpus, here we provide a unified overview with a stronger focus on its constitution. We describe (i) the narrative, chronological, and editorial background of each major sub-corpus, (ii) its semi-diplomatic transcription rules (expansion, normalization, punctuation), (iii) a strategy for challenging out-of-domain test splits driven by outlier detection in a joint embedding space, and (iv) preliminary baseline experiments using TrOCR and MiniCPM2.5 comparing random and outlier-based test partitions. Overall, TRIDIS is designed to stimulate joint robust Handwritten Text Recognition (HTR) and Named Entity Recognition (NER) research across medieval and early modern textual heritage. |
| title | TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Digital Libraries |
| url | https://arxiv.org/abs/2503.22714 |