Towards Historical Map Analysis using Deep Learning Techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969452" target="_blank" >RIV/49777513:23520/23:43969452 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-34111-3_16" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-34111-3_16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-34111-3_16" target="_blank" >10.1007/978-3-031-34111-3_16</a>
Alternative languages
Result language
angličtina
Original language name
Towards Historical Map Analysis using Deep Learning Techniques
Original language description
This paper presents methods for automatic analysis of historical cadastral maps. Our goal is to detect important features in individual map sheets to allow their further processing and connecting the sheets into one seamless map that can bebetter presented online. We concentrate on detection of the map frame,which defines the important segment of the map sheet. Other crucial features are so-called inches that define the measuring scale of the map. We also detect the actual map area. We propose novel segmentation approaches that combine standard computer vision techniques with neural nets (NNs). We have shown that combining the standard computer vision techniques with NNs can outperform the state-of-the-art approaches in the scenario when only little training data is available. We have also created a novel annotated dataset that is used for network training and evaluation.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
AIAI 2023: Artificial Intelligence Applications and Innovations
ISBN
978-3-031-34110-6
ISSN
1868-4238
e-ISSN
1868-422X
Number of pages
13
Pages from-to
173-185
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
León, Španělsko
Event date
Jun 14, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—