Neural Information Extraction from Semi-structured Documents

diciembre 22, 2020 at 11:00 am by

Place: CVC Sala d’Actes | Streaming

Thesis Directors:

Dr. Alicia Fornés (Centre de Visió per Computador, Universitat Autònoma de Barcelona)

Dr. Mauricio Villegas (omni:us – Quidenus Group GmbH)

Dr. Josep Lladós (Centre de Visió per Computador, Universitat Autònoma de Barcelona)

Thesis Committee:

Dr. Joan Andreu Sánchez (Polytechnic University of Valencia)

Dr. Dimosthenis Karatzas (Centre de Visió per Computador, Universitat Autònoma de Barcelona)

Dr. Anjan Dutta (University of Exeter)


Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part.