  Visual information extraction (VIE) has attracted considerable attention
recently owing to its various advanced applications such as document
understanding, automatic marking and intelligent education. Most existing works
decoupled this problem into several independent sub-tasks of text spotting
(text detection and recognition) and information extraction, which completely
ignored the high correlation among them during optimization. In this paper, we
propose a robust visual information extraction system (VIES) towards real-world
scenarios, which is a unified end-to-end trainable framework for simultaneous
text detection, recognition and information extraction by taking a single
document image as input and outputting the structured information.
Specifically, the information extraction branch collects abundant visual and
semantic representations from text spotting for multimodal feature fusion and
conversely, provides higher-level semantic clues to contribute to the
optimization of text spotting. Moreover, regarding the shortage of public
benchmarks, we construct a fully-annotated dataset called EPHOIE
(https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for
both text spotting and visual information extraction. EPHOIE consists of 1,494
images of examination paper head with complex layouts and background, including
a total of 15,771 Chinese handwritten or printed text instances. Compared with
the state-of-the-art methods, our VIES shows significant superior performance
on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used
SROIE dataset under the end-to-end scenario.
