  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.
