  Structure extraction from document images has been a long-standing research
topic due to its high impact on a wide range of practical applications. In this
paper, we share our findings on employing a hierarchical semantic segmentation
network for this task of structure extraction. We propose a prior based deep
hierarchical CNN network architecture that enables document structure
extraction using very high resolution(1800 x 1000) images. We divide the
document image into overlapping horizontal strips such that the network
segments a strip and uses its prediction mask as prior for predicting the
segmentation of the subsequent strip. We perform experiments establishing the
effectiveness of our strip based network architecture through ablation methods
and comparison with low-resolution variations. Further, to demonstrate our
network's capabilities, we train it on only one type of documents (Forms) and
achieve state-of-the-art results over other general document datasets. We
introduce our new human-annotated forms dataset and show that our method
significantly outperforms different segmentation baselines on this dataset in
extracting hierarchical structures. Our method is currently being used in
Adobe's AEM Forms for automated conversion of paper and PDF forms to modern
HTML based forms.
