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
)
```

- 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.

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.

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.