  This article proposes a convenient tool for decoding the output of neural
networks trained by Connectionist Temporal Classification (CTC) for handwritten
text recognition. We use regular expressions to describe the complex structures
expected in the writing. The corresponding finite automata are employed to
build a decoder. We analyze theoretically which calculations are relevant and
which can be avoided. A great speed-up results from an approximation. We
conclude that the approximation most likely fails if the regular expression
does not match the ground truth which is not harmful for many applications
since the low probability will be even underestimated. The proposed decoder is
very efficient compared to other decoding methods. The variety of applications
reaches from information retrieval to full text recognition. We refer to
applications where we integrated the proposed decoder successfully.
