Authors:
Josef Baloun
1
;
2
;
Pavel Král
1
;
2
and
Ladislav Lenc
1
;
2
Affiliations:
1
Department of Computer Science and Engineering, University of West Bohemia, Univerzitní, Pilsen, Czech Republic
;
2
NTIS - New Technologies for the Information Society, University of West Bohemia, Univerzitní, Pilsen, Czech Republic
Keyword(s):
Page Segmentation, Dataset, Chronicle, Historical Document, Image, Text, Background, Fully Convolutional Neural Network, Pixel Labeling, Artificial Page.
Abstract:
The segmentation of document images plays an important role in the process of making their content electronically accessible. This work focuses on the segmentation of historical handwritten documents, namely chronicles. We take image, text and background classes into account. For this goal, a new dataset is created mainly from chronicles provided by Porta fontium. In total, the dataset consists of 58 images of document pages and their precise annotations for text, image and graphic regions in PAGE format. The annotations are also provided at a pixel level. Further, we present a baseline evaluation using an approach based on a fully convolutional neural network. We also perform a series of experiments in order to identify the best method configuration. It includes a novel data augmentation method which creates artificial pages.