  The Connectionist Temporal Classification (CTC) has achieved great success in
sequence to sequence analysis tasks such as automatic speech recognition (ASR)
and scene text recognition (STR). These applications can use the CTC objective
function to train the recurrent neural networks (RNNs), and decode the outputs
of RNNs during inference. While hardware architectures for RNNs have been
studied, hardware-based CTCdecoders are desired for high-speed CTC-based
inference systems. This paper, for the first time, provides a low-complexity
and memory-efficient approach to build a CTC-decoder based on the beam search
decoding. Firstly, we improve the beam search decoding algorithm to save the
storage space. Secondly, we compress a dictionary (reduced from 26.02MB to
1.12MB) and use it as the language model. Meanwhile searching this dictionary
is trivial. Finally, a fixed-point CTC-decoder for an English ASR and an STR
task using the proposed method is implemented with C++ language. It is shown
that the proposed method has little precision loss compared with its
floating-point counterpart. Our experiments demonstrate the compression ratio
of the storage required by the proposed beam search decoding algorithm are
29.49 (ASR) and 17.95 (STR).
