CITlab ARGUS for historical handwritten documents. 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.
References in zbMATH (referenced in 1 article )
Showing result 1 of 1.
- Strauß, Tobias; Leifert, Gundram; Grüning, Tobias; Labahn, Roger: Regular expressions for decoding of neural network outputs (2016)