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:相比于一般的文档分析任务,表格文档结构的理解和检索更具挑战性。 表格文档通常由两种类型的作者制作:一个是表单设计师,他开发表单结构和键值;另一个是表单用户,他根据提供的键值填写表单值。因此,如果表单用户困惑了,表单值可能与表单设计师的意图(结构和键值)不一致。在本文中,我们介绍了Form-NLU,这是一个用于表单结构理解及其键值信息提取的全新数据集,用于解释表单设计者的意图,并将用户编写的值与它对齐。它包括857张表格图像、6k 表格键和值和4k 表格键和值。我们的数据集还包括三种表格类型:数字、印刷和手写,涵盖了多种表格外观和布局。我们提出了一种强大的基于位置和逻辑关系的表格键值信息提取框架。使用这个数据集,我们首先检查了对于表格布局理解强大的物体检测模型,然后在数据集上评估了键信息提取任务,并为不同类型的表格和键提供了细粒度的结果。此外,我们使用现成的pdf布局提取工具对其进行了验证,并证明了其在实际案例中的可行性。