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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.08636 |
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| _version_ | 1866913938345558016 |
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| author | Bottaioli, Natalia Tarride, Solène Anger, Jérémy Mowlavi, Seginus Gardella, Marina Tadros, Antoine Facciolo, Gabriele von Gioi, Rafael Grompone Kermorvant, Christopher Morel, Jean-Michel Preciozzi, Javier |
| author_facet | Bottaioli, Natalia Tarride, Solène Anger, Jérémy Mowlavi, Seginus Gardella, Marina Tadros, Antoine Facciolo, Gabriele von Gioi, Rafael Grompone Kermorvant, Christopher Morel, Jean-Michel Preciozzi, Javier |
| contents | This study evaluates the recently proposed Document Attention Network (DAN) for extracting key-value information from Uruguayan birth certificates, handwritten in Spanish. We investigate two annotation strategies for automatically transcribing handwritten documents, fine-tuning DAN with minimal training data and annotation effort. Experiments were conducted on two datasets containing the same images (201 scans of birth certificates written by more than 15 different writers) but with different annotation methods. Our findings indicate that normalized annotation is more effective for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates Bottaioli, Natalia Tarride, Solène Anger, Jérémy Mowlavi, Seginus Gardella, Marina Tadros, Antoine Facciolo, Gabriele von Gioi, Rafael Grompone Kermorvant, Christopher Morel, Jean-Michel Preciozzi, Javier Computer Vision and Pattern Recognition Artificial Intelligence This study evaluates the recently proposed Document Attention Network (DAN) for extracting key-value information from Uruguayan birth certificates, handwritten in Spanish. We investigate two annotation strategies for automatically transcribing handwritten documents, fine-tuning DAN with minimal training data and annotation effort. Experiments were conducted on two datasets containing the same images (201 scans of birth certificates written by more than 15 different writers) but with different annotation methods. Our findings indicate that normalized annotation is more effective for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized. |
| title | Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.08636 |