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Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela str., Jelgava, LV-3001, Latvia

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1 Papers

A Flexible Model for Enterprise Document Capturing Automation

Topics: Intelligent Agents; Models and Frameworks; Software Metrics and Measurement

Authors: Juris Rāts ¹ , Inguna Pede ¹ , Tatjana Rubina ² and Gatis Vītols ²

Affiliations: ¹ RIX Technologies, Blaumana 5a-3, Riga, LV-1011, Latvia , ² Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela str., Jelgava, LV-3001, Latvia

Digital Object Identifier: 10.5220/0009034802970304

Proceedings of ICEIS 2020, ISBN: 978-989-758-423-7

Quick Abstract

The aim of the research is to create and evaluate a flexible model for document capturing that would employ machine learning to classify documents feeding them with values for one or more metadata items. Documents and classification metadata fields typical for Enterprise Content Management (ECM) systems are used in the research. The model comprises selection of classification methods, configuration of the methods hyperparameters and configuration of a number of other learning related parameters. The model provides user with visual means to analyse the classification outcomes and those to tune the further steps of the learning. A couple of challenges are addressed along the way – as informal and eventually changing criteria for document classification, and imbalanced data sets.