CITlab ARGUS for historical handwritten documents
Gundram Leifert, Tobias Strau{\ss}, Tobias Gr\"uning, Roger, Labahn
TL;DR
This paper presents CITlab's recognition system for historical handwritten documents, utilizing MDRNNs and CTC, integrated within the PLANET ARGUS framework, to improve recognition accuracy in the ICDAR 2015 competition.
Contribution
The paper introduces a recognition system based on MDRNNs and CTC, integrated into the ARGUS framework, for improved historical handwritten text recognition.
Findings
Effective recognition of historical handwritten documents.
Integration of MDRNN and CTC within the ARGUS framework.
Participation in ICDAR 2015 HTRtS competition.
Abstract
We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. 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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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · 3D Surveying and Cultural Heritage