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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.