CITlab ARGUS for historical handwritten documents
This is an incremental application of existing methods to a domain-specific task in document analysis.
The paper tackled the recognition of historical handwritten documents in the HTRtS competition at ICDAR 2015, using a system based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC), but did not report specific performance numbers or results.
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.