Resumen de Extracció d'informació de documents semi-estructurats amb models neurals
Sectors as fintech, legaltech or insurance process an inflow of million of forms, invoices, id documents, claims or similar every day. The success in the automation of these transactions depends on the ability to correctly digitize the textual content as well as to incorporate semantic understanding. 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.
