An approach for output decoding of neural networks Strauß, Tobias , 1984- (VerfasserIn) Labahn, Roger , 1959- (VerfasserIn) Leifert, Gundram (VerfasserIn) Wustlich, Welf (VerfasserIn) Text Text 2012 2012 eng electronic resourceremoteComputermedienOnline-Ressource1 Online-Ressource (7 Seiten) We review two decoding methods for recurrent neural networks – based on the well known weighted Levenshtein distance and the connectionist temporal classification (CTC) – and we introduce a new method – the dynamic Levenstein distance (DynWL). We compare these three methods analytically in terms of time complexity and error performance. Although the approaches are different, there are deep connections between These ways of decoding. Finally, we test on the Arabic and French ICDAR data sets. Our experiments show that CTC yields the smallest error rates. Nevertheless, there are scenarios where DynWL is a good choice between performance and time complexity.<eng> Open Access Urheberrechtsschutz 1.0 Tobias Strauß, Roger Labahn, Gundram Leifert, Welf Wustlich 510 https://purl.uni-rostock.de/rosdok/id00005820 urn:nbn:de:gbv:28-rosdok_id00005820-5 doi: 10.18453/rosdok_id00005820 oclc: 1621163060 ppn: (DE-627)1986180786