CITlab ARGUS for historical handwritten documents
Tobias Strau{\ss}, Tobias Gr\"uning, Gundram Leifert, Roger Labahn, (for the University of Rostock - CITlab)
TL;DR
This paper presents CITlab's recognition system for historical handwritten documents, utilizing MDRNNs and CTC within the ARGUS framework, achieving effective recognition in the ICFHR 2014 competition.
Contribution
It introduces a recognition system based on MDRNNs and CTC integrated into the ARGUS framework for historical handwritten text recognition.
Findings
Effective recognition of historical handwritten documents
Utilization of MDRNNs and CTC algorithms
Integration within the ARGUS framework
Abstract
We describe CITlab's recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algorithms 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.
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