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Extracting key information from documents, such as receipts or invoices, andpreserving the interested texts to structured data is crucial in thedocument-intensive streamline processes of office automation in areas thatincludes but not limited to accounting, financial, and taxation areas. To avoiddesigning expert rules for each specific type of document, some published worksattempt to tackle the problem by learning a model to explore the semanticcontext in text sequences based on the Named Entity Recognition (NER) method inthe NLP field. In this paper, we propose to harness the effective informationfrom both semantic meaning and spatial distribution of texts in documents.Specifically, our proposed model, Convolutional Universal Text InformationExtractor (CUTIE), applies convolutional neural networks on gridded texts wheretexts are embedded as features with semantical connotations. We further explorethe effect of employing different structures of convolutional neural networkand propose a fast and portable structure. We demonstrate the effectiveness ofthe proposed method on a dataset with up to $4,484$ labelled receipts, withoutany pre-training or post-processing, achieving state of the art performancethat is much better than the NER based methods in terms of either speed andaccuracy. Experimental results also demonstrate that the proposed CUTIE modelbeing able to achieve good performance with a much smaller amount of trainingdata.

CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor