Key-value Pair Extraction

SIBR是面向自然场景视觉信息抽取的数据集。 1）SIBR总的有1000张图片，400张测试，600张训练，包括中文、英文两种语言。 2）包含images.zip、label.zip、train.txt、test.txt四个文件，images.zip、label.zip中包含所有图片和标签，通过train.txt和test.txt区分训练和测试。 3）标注规则与FUNSD、XFUND一致，实体类别包括header、question、answer、other四类，但与之不同的是除了提供link id之外，还标注了link type。link type包括inter以及intra两种，inter表示实体间的link，也即是一对kv对之间的link；而intra表示同一个实体内部segment之间的link。通过intra将多行文字组成一个实体，通过inter将实体组成kv对。

RFUND is a relabeled version of FUNSD and XFUND datasets, tackling the following issues in their original annotations: 1. Entity (block) level OCR results. Real-world OCR engines usually produce line-level results, while the annotations in FUNSD and XFUND are at the entity (block) level. Text lines within the same entity are aggregated and serialized in human reading order, simplifying the task scope and failing to reflect real-world challenges. 2. Inconsistent labelling granularity. In FUNSD, while most contents are annotated at the entity level, multi-line entities with first-line indentation are annotated separately, in which the first line is split out and the rest are aggregated. XFUND exhibits variable granularity in annotations, with some contents labelled at the entity level and others at the line level. Such inconsistent labelling standards can hinder model training. 3. Erroneous category annotations. Entities in FUNSD/XFUND are categorized as "header", "question", "answer" and "other". We observed that certain entities in both FUNSD and XFUND have category labels that differ from human understanding. In the following example, the answer entity “Client confirmed agreement ...” was labelled as other, while the other entity “CONFIDENTIAL” was labelled as the question.

English subset of RFUND