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End-to-end information extraction from documents

We propose the Attend, Copy, Parse architecture, a deep neural network model trained directly on end-to-end data, bypassing the need for word-level labels.
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Authors: Rasmus Berg Palm, Florian Laws, Ole Winther
Publication Date: December 18th 2018

Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs.

This end-to-end data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost.

Unfortunately, state-of-the-art word classification methods for information extraction cannot use this data, instead requiring word-level labels which are expensive to create and consequently not available for many real life tasks.

In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on end-to-end data, bypassing the need for word-level labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a state-of-the-art production system based on word classification.

We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required word-level labels.

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