An Interpretable Deep Learning Approach for Morphological Script Type Analysis
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910571386896384 |
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| author | Vlachou-Efstathiou, Malamatenia Siglidis, Ioannis Stutzmann, Dominique Aubry, Mathieu |
| author_facet | Vlachou-Efstathiou, Malamatenia Siglidis, Ioannis Stutzmann, Dominique Aubry, Mathieu |
| contents | Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11150 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Interpretable Deep Learning Approach for Morphological Script Type Analysis Vlachou-Efstathiou, Malamatenia Siglidis, Ioannis Stutzmann, Dominique Aubry, Mathieu Computer Vision and Pattern Recognition Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis |
| title | An Interpretable Deep Learning Approach for Morphological Script Type Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11150 |