International Conference on Frontiers in Handwriting Recognition, ICFHR 2010, Kolkata, India, 16-18 November 2010
نویسندگان
چکیده
منابع مشابه
CITlab ARGUS for historical handwritten documents
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 multidimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software m...
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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 multidimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software m...
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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 The task comprises word recognition from segmented historical documents.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 s...
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Handwriting is considered to be one of the commonly used modality to identify persons in commercial, governmental and forensic applications. In order to record recent advances in the field of writer identification, we are proposing to organize the ICDAR2015 writer identification competition using KHATT, AHTID/MW and IBHC Databases. A first edition of the Arabic Writer Identification Competition...
متن کاملCITlab ARGUS for historical data tables
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 The task comprises word recognition from segmented historical documents.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 s...
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تاریخ انتشار 2010