CITlab ARGUS for historical data tables : Description of CITlab’s System for the ANWRESH-2014 Word Recognition Task

Gundram Leifert; Tobias Grüning; Tobias Strauß; Roger Labahn

doi:10.18453/rosdok_id00005818

zum übergeordneten Dokument

Gundram Leifert Tobias Grüning Tobias Strauß Roger Labahn

CITlab ARGUS for historical data tables : Description of CITlab’s System for the ANWRESH-2014 Word Recognition Task

University of Rostock, Institute of Mathematics, 2014

https://doi.org/10.18453/rosdok_id00005818

Abstract: We describe CITlab’s recognition system for the ANWRESH-2014 Competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmented historical documents. The core components of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET’s ARGUS framework for intelligent text recognition and image processing.

Workingpaper Freier Zugang

Titel:

CITlab ARGUS for historical data tables: Description of CITlab’s System for the ANWRESH-2014 Word Recognition Task

Schriftenreihe:

Preprints aus dem Institut für Mathematik, 14/1

Beteiligte Personen:

Gundram Leifert[VerfasserIn]
Tobias Grüning[VerfasserIn]
	1181883350
Tobias Strauß[VerfasserIn]
	0000-0002-5344-4188
	1136294554
Roger Labahn[VerfasserIn]
	0000-0003-1901-9644
	170999645

Zusammenfassung:

We describe CITlab’s recognition system for the ANWRESH-2014 Competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmented historical documents. The core components of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET’s ARGUS framework for intelligent text recognition and image processing. [Englisch]

Dokumenttyp:

Workingpaper

Einrichtung:

Mathematisch-Naturwissenschaftliche Fakultät

Sprache:

Englisch

Sachgruppe der DNB:

510 Mathematik

Umfang:

1 Online-Ressource (7 Seiten)

Veröffentlichung /
Entstehung:

Rostock: University of Rostock, Institute of Mathematics