University of Twente Student Theses

Login

Predicting Semantic Labels of Text Regions in Heterogeneous Document Images

Enendu, Somtochukwu (2019) Predicting Semantic Labels of Text Regions in Heterogeneous Document Images.

[img]
Preview
PDF
7MB
Abstract:This paper describes the use of sequence labeling methods in predicting the semantic labels of extracted text regions of heterogeneous electronic documents, by utilizing features related to each semantic label. In this study, we construct a novel dataset consisting of real world documents from multiple domains. We test the performance of the methods on the dataset and offer a novel investigation into the influence of textual features on performance across multiple domains. The results of the experiments show that the Conditional Random Field method is robust, outperforming the neural network when limited training data is available. Regarding generalizability, our experiments show that the inclusion of textual features does not guarantee performance improvements.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/79319
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page