  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.
