×VerfasserIn
	0000-0002-5344-4188
	1136294554

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	0000-0003-1901-9644
	170999645

Tobias Strauß     Roger Labahn     Gundram Leifert     Welf Wustlich

An approach for output decoding of neural networks

University of Rostock, Institute of Mathematics, 2012

https://doi.org/10.18453/rosdok_id00005820

Abstract: 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.

working paper   free access