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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.02235 |
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| _version_ | 1866929750239346688 |
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| author | Barboule, Camille Piwowarski, Benjamin Chabot, Yoan |
| author_facet | Barboule, Camille Piwowarski, Benjamin Chabot, Yoan |
| contents | The field of visually-rich document understanding, which involves interacting with visually-rich documents (whether scanned or born-digital), is rapidly evolving and still lacks consensus on several key aspects of the processing pipeline. In this work, we provide a comprehensive overview of state-of-the-art approaches, emphasizing their strengths and limitations, pointing out the main challenges in the field, and proposing promising research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends Barboule, Camille Piwowarski, Benjamin Chabot, Yoan Computation and Language The field of visually-rich document understanding, which involves interacting with visually-rich documents (whether scanned or born-digital), is rapidly evolving and still lacks consensus on several key aspects of the processing pipeline. In this work, we provide a comprehensive overview of state-of-the-art approaches, emphasizing their strengths and limitations, pointing out the main challenges in the field, and proposing promising research directions. |
| title | Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.02235 |