  Extracting key information from documents, such as receipts or invoices, and
preserving the interested texts to structured data is crucial in the
document-intensive streamline processes of office automation in areas that
includes but not limited to accounting, financial, and taxation areas. To avoid
designing expert rules for each specific type of document, some published works
attempt to tackle the problem by learning a model to explore the semantic
context in text sequences based on the Named Entity Recognition (NER) method in
the NLP field. In this paper, we propose to harness the effective information
from both semantic meaning and spatial distribution of texts in documents.
Specifically, our proposed model, Convolutional Universal Text Information
Extractor (CUTIE), applies convolutional neural networks on gridded texts where
texts are embedded as features with semantical connotations. We further explore
the effect of employing different structures of convolutional neural network
and propose a fast and portable structure. We demonstrate the effectiveness of
the proposed method on a dataset with up to $4,484$ labelled receipts, without
any pre-training or post-processing, achieving state of the art performance
that is much better than the NER based methods in terms of either speed and
accuracy. Experimental results also demonstrate that the proposed CUTIE model
being able to achieve good performance with a much smaller amount of training
data.
