## CTCGreedyDecoderSeqLen <a name="CTCGreedyDecoderSeqLen"></a> {#openvino_docs_ops_sequence_CTCGreedyDecoderSeqLen_6}

**Versioned name**: *CTCGreedyDecoderSeqLen-6*

**Category**: Sequence processing

**Short description**: *CTCGreedyDecoderSeqLen* performs greedy decoding of the logits provided as the first input. The sequence lengths are provided as the second input.

**Detailed description**:

This operation is similar to the [TensorFlow CTCGreedyDecoder](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder).

The operation *CTCGreedyDecoderSeqLen* implements best path decoding.
Decoding is done in two steps:

1. Concatenate the most probable classes per time-step which yields the best path.

2. Remove duplicate consecutive elements if the attribute *merge_repeated* is true and then remove all blank elements.

Sequences in the batch can have different length. The lengths of sequences are coded in the second input integer tensor `sequence_length`.

The main difference between [CTCGreedyDecoder](CTCGreedyDecoder_1.md) and CTCGreedyDecoderSeqLen is in the second input. CTCGreedyDecoder uses 2D input floating point tensor with sequence masks for each sequence in the batch while CTCGreedyDecoderSeqLen uses 1D integer tensor with sequence lengths.

**Attributes**

* *merge_repeated*

  * **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC calculation. If the value is false the sequence `ABB*B*B`  (where '*' is the blank class) will look like `ABBBB`. But if the value is true, the sequence will be `ABBB`.
  * **Range of values**: true or false
  * **Type**: `boolean`
  * **Default value**: true
  * **Required**: *No*
    
* *classes_index_type*

  * **Description**: the type of output tensor with classes indices
  * **Range of values**: "i64" or "i32"
  * **Type**: string
  * **Default value**: "i32"
  * **Required**: *No*
  
* *sequence_length_type*

  * **Description**: the type of output tensor with sequence length
  * **Range of values**: "i64" or "i32"
  * **Type**: string
  * **Default value**: "i32"
  * **Required**: *No*

**Inputs**

* **1**: `data` - input tensor of type *T_F* of shape `[N, T, C]` with a batch of sequences. Where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.**

* **2**: `sequence_length` - input tensor of type *T_I* of shape `[N]` with sequence lengths. The values of sequence length must be less or equal to `T`. **Required.**

* **3**: `blank_index` - scalar or 1D tensor with 1 element of type *T_I*. Specifies the class index to use for the blank class. Regardless of the value of `merge_repeated` attribute, if the output index for a given batch and time step corresponds to the `blank_index`, no new element is emitted. Default value is `C-1`. **Optional**.

**Output**

* **1**: Output tensor of type *T_IND1* shape `[N, T]` and containing the decoded classes. All elements that do not code sequence classes are filled with -1.

* **2**: Output tensor of type *T_IND2* shape `[N]` and containing length of decoded class sequence for each batch.

**Types**

* *T_F*: any supported floating point type.

* *T_I*: `int32` or `int64`.

* *T_IND1*: `int32` or `int64` and depends on `classes_index_type` attribute.

* *T_IND2*: `int32` or `int64` and depends on `sequence_length_type` attribute.

**Example**

```xml
<layer type="CTCGreedyDecoderSeqLen" merge_repeated="true" classes_index_type="i64" sequence_length_type="i64">
    <input>
        <port id="0">
            <dim>8</dim>
            <dim>20</dim>
            <dim>128</dim>
        </port>
        <port id="1">
            <dim>8</dim>
        </port>
        <port id="2"/>  <!-- blank_index = 120 -->
    </input>
    <output>
        <port id="0" precision="I64">
            <dim>8</dim>
            <dim>20</dim>
        </port>
        <port id="1" precision="I64">
            <dim>8</dim>
        </port>
    </output>
</layer>
```
