KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents

TL;DR

This paper introduces KVP10k, a large, diverse dataset and benchmark for extracting key-value pairs from business documents without predefined keys, addressing a significant gap in existing resources.

Contribution

It presents the first comprehensive dataset and benchmark specifically designed for non-predetermined key-value pair extraction in complex business documents.

Findings

KVP10k contains 10,707 annotated images.

The benchmark challenges models to extract KVPs without predefined keys.

The dataset enhances diversity and annotation detail for better model training.

Abstract

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.