Title :
Document segmentation using Relative Location Features
Author :
Fernandez, F.C. ; Terrades, O.R.
Author_Institution :
Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain
Abstract :
In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.
Keywords :
document image processing; feature extraction; image segmentation; RLF; document structure; historical document segmentation; relative location feature; Adaptation models; Computational modeling; Frequency domain analysis; Image segmentation; Labeling; Minimization; Training;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4