Learning to Extract Semantic Structure from Documents
Using Multimodal Fully Convolutional Neural Networks
Abstract
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure
extraction as a pixel-wise segmentation task, and propose a
unified model that classifies pixels based not only on their
visual appearance, as in the traditional page segmentation
task, but also on the content of underlying text. Moreover,
we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic
documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that
both our multimodal approach and the synthetic data pretraining significantly boost the performance