Can use either greedy search (also known as best path) or a constrained
dictionary search.

k_ctc_decode(y_pred, input_length, greedy = TRUE, beam_width = 100L,
top_paths = 1)

## Arguments

y_pred |
tensor `(samples, time_steps, num_categories)` containing the
prediction, or output of the softmax. |

input_length |
tensor `(samples, )` containing the sequence length for
each batch item in `y_pred` . |

greedy |
perform much faster best-path search if `TRUE` . This does not
use a dictionary. |

beam_width |
if `greedy` is `FALSE` : a beam search decoder will be used
with a beam of this width. |

top_paths |
if `greedy` is `FALSE` , how many of the most probable paths
will be returned. |

## Value

If `greedy`

is `TRUE`

, returns a list of one element
that contains the decoded sequence. If `FALSE`

, returns the `top_paths`

most probable decoded sequences. Important: blank labels are returned as
`-1`

. Tensor `(top_paths)`

that contains the log probability of each
decoded sequence.

## Keras Backend

This function is part of a set of Keras backend functions that enable
lower level access to the core operations of the backend tensor engine
(e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here:
https://keras.rstudio.com/articles/backend.html#backend-functions.