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Building a test collection for complex document information processing
Abstract
Research and development of information access technology for scanned paper documents has been hampered by the lack of public test collections of realistic scope and complexity. As part of a project to create a prototype system for search and mining of masses of document images, we are assembling a 1.5 terabyte dataset to support evaluation of both end-to-end complex document information processing (CDIP) tasks (e.g., text retrieval and data mining) as well as component technologies such as optical character recognition (OCR), document structure analysis, signature matching, and authorship attribution.
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Cited by
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403172 · Cited by 812
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Huang, Y., Lv, T., Cui, L., Lu, Y., & Wei, F. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3548112 · Cited by 719
(2022) · doi:10.1007/978-3-031-19815-1_29 · Cited by 469
DocFormer: End-to-End Transformer for Document Understanding
Appalaraju, S., Jasani, B., Kota, B. U., Xie, Y., & Manmatha, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00103 · Cited by 363
Sequence-aware multimodal page classification of Brazilian legal documents
Luz de Araujo, P. H., de Almeida, A. P. G. S., Ataides Braz, F., Correia da Silva, N., de Barros Vidal, F., & de Campos, T. E. (2023) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-022-00406-7 · Cited by 9
OpenPSS: An Open Page Stream Segmentation Benchmark
Heusden, R. V., Kamps, J., & Marx, M. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72437-4_24 · Cited by 3
Metadata
- Authors
- Lewis, D., Agam, G., Argamon, S., Frieder, O., Grossman, D., & Heard, J.
- Venue
- Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2006-08-06
- Pages
- 665-666
- OpenAlex
- W1966382373
- Cited by
- 286
- References
- 0
- FWCI
- 4.52
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
