## CTCGreedyDecoder {#openvino_docs_ops_sequence_CTCGreedyDecoder_1} **Versioned name**: *CTCGreedyDecoder-1* **Category**: Sequence processing **Short description**: *CTCGreedyDecoder* performs greedy decoding on the logits given in input (best path). **Detailed description**: This operation is similar [Reference](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder) Given an input sequence \f$X\f$ of length \f$T\f$, *CTCGreedyDecoder* assumes the probability of a length \f$T\f$ character sequence \f$C\f$ is given by \f[ p(C|X) = \prod_{t=1}^{T} p(c_{t}|X) \f] Sequences in the batch can have different length. The lengths of sequences are coded as values 1 and 0 in the second input tensor `sequence_mask`. Value `sequence_mask[j, i]` specifies whether there is a sequence symbol at index `i` in the sequence `i` in the batch of sequences. If there is no symbol at `j`-th position `sequence_mask[j, i] = 0`, and `sequence_mask[j, i] = 1` otherwise. Starting from `j = 0`, `sequence_mass[j, i]` are equal to 1 up to the particular index `j = last_sequence_symbol`, which is defined independently for each sequence `i`. For `j > last_sequence_symbol`, values in `sequence_mask[j, i]` are all zeros. **Attributes** * *merge_repeated* * **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC calculation. * **Range of values**: True or False * **Type**: `boolean` * **Default value**: True * **Required**: *no* **Inputs** * **1**: `data` - Input tensor with a batch of sequences. Type of elements is any supported floating point type. Shape of the tensor is `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. Required. * **2**: `sequence_mask` - 2D input floating point tensor with sequence masks for each sequence in the batch. Populated with values 0 and 1. Shape of this input is `[T, N]`. Required. **Output** * **1**: Output tensor with shape `[N, T, 1, 1]` and integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with -1. Type of elements is floating point, but all values are integers. **Example** ```xml 20 8 128 20 8 8 20 1 1 ```