  Document structure extraction has been a widely researched area for decades
with recent works performing it as a semantic segmentation task over document
images using fully-convolution networks. Such methods are limited by image
resolution due to which they fail to disambiguate structures in dense regions
which appear commonly in forms. To mitigate this, we propose Form2Seq, a novel
sequence-to-sequence (Seq2Seq) inspired framework for structure extraction
using text, with a specific focus on forms, which leverages relative spatial
arrangement of structures. We discuss two tasks; 1) Classification of low-level
constituent elements (TextBlock and empty fillable Widget) into ten types such
as field captions, list items, and others; 2) Grouping lower-level elements
into higher-order constructs, such as Text Fields, ChoiceFields and
ChoiceGroups, used as information collection mechanism in forms. To achieve
this, we arrange the constituent elements linearly in natural reading order,
feed their spatial and textual representations to Seq2Seq framework, which
sequentially outputs prediction of each element depending on the final task. We
modify Seq2Seq for grouping task and discuss improvements obtained through
cascaded end-to-end training of two tasks versus training in isolation.
Experimental results show the effectiveness of our text-based approach
achieving an accuracy of 90% on classification task and an F1 of 75.82, 86.01,
61.63 on groups discussed above respectively, outperforming segmentation
baselines. Further we show our framework achieves state of the results for
table structure recognition on ICDAR 2013 dataset.
