CITlab ARGUS for historical data tables
Gundram Leifert, Tobias Gr\"uning, Tobias Strau{\ss}, Roger Labahn, (for the University of Rostock - CITlab)
TL;DR
This paper presents CITlab's recognition system for historical document word recognition, utilizing MDRNNs and CTC within the ARGUS framework, achieving effective recognition in the ICFHR 2014 competition.
Contribution
The paper introduces a system combining MDRNNs and CTC for historical handwritten text recognition, integrated within the ARGUS framework, demonstrating competitive performance.
Findings
Effective word recognition on historical documents
Integration of MDRNN and CTC techniques
Utilization of ARGUS framework for recognition tasks
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 The task comprises word recognition from segmented historical documents.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.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · Natural Language Processing Techniques