license: other
license_name: cdla-permissive-2.0
license_link: https://cdla.dev/permissive-2-0/
tags:
- document-ai
pretty_name: KVP10k
size_categories:
- 10K<n<100K
KVP10k
A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents.
Abstract for the paper describing this dataset:
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attracted substantial interest from both industry and academy, highlighting its significance in the current technological landscape. Most datasets in this area are primarily focused on Key Information Extraction (KIE), where the extraction process revolves around extracting information using a specific, predefined set of keys. Unlike most existing datasets and benchmarks, our focus is on discovering key-value pairs (KVPs) without relying on predefined keys, navigating through an array of diverse templates and complex layouts. This task presents unique challenges, primarily due to the absence of comprehensive datasets and benchmarks tailored for non-predetermined KVP extraction. To address this gap, we introduce KVP10k , a new dataset and benchmark specifically designed for KVP extraction. The dataset contains 10707 richly annotated images. In our benchmark, we also introduce a new challenging task that combines elements of KIE as well as KVP in a single task. KVP10k sets itself apart with its extensive diversity in data and richly detailed annotations, paving the way for advancements in the field of information extraction from complex business documents.
Dataset Details
Dataset Description
- Curated by: IBM
- License: cdla-permissive-2.0
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Source Data
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Recommendations
Citation
BibTeX:
@misc{naparstek2024kvp10k,
title={KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents},
author={Oshri Naparstek and Roi Pony and Inbar Shapira and Foad Abo Dahood and Ophir Azulai and Yevgeny Yaroker and Nadav Rubinstein and Maksym Lysak and Peter Staar and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Elad Amrani and Idan Friedman and Orit Prince and Yevgeny Burshtein and Adi Raz Goldfarb and Udi Barzelay},
year={2024},
eprint={2405.00505},
archivePrefix={arXiv},
primaryClass={cs.IR}
}