Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.

翻译：摘要：与通用文档分析任务相比，表单文档的结构理解与检索更具挑战性。表单文档通常由两类作者共同完成：表单设计者负责构建表单结构及键（keys），而表单用户则依据所提供键填写表单值（values）。因此，若表单用户产生困惑，其填写的值可能偏离设计者的预期结构与键。本文提出Form-NLU，这是首个面向表单结构理解及其键值信息抽取的新型数据集，旨在解读设计者意图并评估用户填写内容与其的对齐程度。该数据集包含857张表单图像、6000个表单键值对及4000个表格键值对，涵盖数字式、印刷式与手写式三种表单类型，覆盖多样外观与布局。我们提出一种基于位置与逻辑关系的鲁棒表单键值信息抽取框架。基于Form-NLU数据集，我们首先评估了强目标检测模型在表单布局理解中的表现，随后在数据集上评测键信息抽取任务，并针对不同表单类型与键类别提供细粒度分析结果。此外，我们利用现成的PDF布局提取工具验证了该框架在现实场景中的可行性。