diff --git a/docs/ops/sequence/CTCLoss_4.md b/docs/ops/sequence/CTCLoss_4.md
index 6b6effe760f7db..2288413322d998 100644
--- a/docs/ops/sequence/CTCLoss_4.md
+++ b/docs/ops/sequence/CTCLoss_4.md
@@ -10,14 +10,13 @@
 
 This operation is similar to the TensorFlow* operation [CTCLoss](https://www.tensorflow.org/api_docs/python/tf/compat/v1/nn/ctc_loss)
 
-*CTCLoss* calculates loss between input and target sequences. It sums over the probability of possible alignments of input to target.
+*CTCLoss* estimates a chance that a target can occur (or is real) for given input sequence of logits.
+Briefly, *CTCLoss* operation finds all sequences aligned with a target sequence `labels[i,:]`, computes log-probabilities of these aligned sequences using `inputs[:,i,:]` of logits
+and computes a negative sum of these log-probabilies.
 
-Input sequences `inputs[:,i,:]` 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 `j` 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.
-A length of target sequence from `labels[i,:]` must not be greater than a lenght of corresponding input sequence. Otherwise, the operation behaviour is undefined.
+Input sequences of logits from `inputs` can have different length. The length of each sequence `inputs[:,i,:]` equals `input_length[i]`.
+A length of target sequence from `labels[i,:]` is determined by a pad `-1`. The length must not be greater than a lenght of corresponding input sequence `inputs[:,i,:]`.
+Otherwise, the operation behaviour is undefined.
 
 *CTCLoss* calculation scheme:
 
@@ -29,18 +28,19 @@ p_{t,i,j} = \frac{\exp(inputs[t,i,j])}{\sum^{K}_{k=0}{\exp(inputs[t,i,k])}}
 2. For a given `i`-th target from `labels[i,:]` find all aligned paths.
 A path `S = (c1,c2,...,cT)` is aligned with a target `G=(g1,g2,...,gT)` if both chains are equal after decoding.
 The decoding removes pads `-1` from a target `G` and merges repeated characters in `G` in case *preprocess_collapse_repeated* equal to True.
-The decoding merges repeated characters in `S` in case *merge_repeated* equal to True and removes blank characters represented by last class index `K-1`, 
-where `K` is a number of classes including a blank symbol.
-For example, in case default *merge_repeated* and *preprocess_collapse_repeated* a target sequence `(0,3,2,2,-1,-1,-1,-1,-1)` is processed to `(0,3,2,2)` and
-an input `(0,0,4,3,2,2,4,2,4)` is also processed to `(0,3,2,2)`, where `K=5`.
+The decoding merges repeated characters in `S` in case *ctc_merge_repeated* equal to True and removes blank characters represented by `blank_index`.
+By default, `blank_index` is equal to `C-1`, where `C` is a number of classes including the blank.
+For example, in case default *ctc_merge_repeated*, *preprocess_collapse_repeated*, *unique*, and `blank_index` a target sequence `(0,3,2,2,-1,-1,-1,-1,-1)` is processed to `(0,3,2,2)` and
+a path `(0,0,4,3,2,2,4,2,4)` is also processed to `(0,3,2,2)`, where `C=5`. There exist other paths that are also aligned, for instance, `0,4,3,3,2,4,2,2,2`.
+Paths checked for alignment with `label[:,i]` must be of length `input_length[i] = L_i`.
 Compute probabilities of these alignments as follows:
 \f[
-p(S) = \prod_{t=1}^{T} p_{t,i,ct}
+p(S) = \prod_{t=1}^{L_i} p_{t,i,ct}
 \f]
 
-3. Finally sum up probabilities of all aligned paths for a given target and compute negative logarithm of it:
+3. Finally sum up logarithms of probabilities of all aligned paths with negative sign:
 \f[
-CTCLoss = -\log \sum_{S} p(S)
+CTCLoss = - \sum_{S} \ln p(S)
 \f]
 
 
@@ -54,22 +54,32 @@ CTCLoss = -\log \sum_{S} p(S)
   * **Default value**: False
   * **Required**: *no*
 
-* *merge_repeated*
+* *ctc_merge_repeated*
 
-  * **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC loss calculation.
+  * **Description**: *ctc_merge_repeated* is a flag for merging repeated labels during the CTC loss calculation.
   * **Range of values**: True or False
   * **Type**: `boolean`
   * **Default value**: True
   * **Required**: *no*
 
+* *unique*
+
+  * **Description**: *unique* is a flag to find unique elements for each `labels[i,:]` before matching with potential alignments2.
+  * **Range of values**: True or False
+  * **Type**: `boolean`
+  * **Default value**: False
+  * **Required**: *no*
+
 **Inputs**
 
-* **1**: `inputs` - Input tensor with a batch of sequences. Type of elements is *T_F*. 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.
+* **1**: `inputs` - Input tensor with a batch of sequences. Type of elements is *T_F*. 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 including the blank. Required.
 
-* **2**: `sequence_mask` - 2D input tensor of type *T_IND* with sequence masks for each sequence in the batch. Populated with values 0 and 1. Shape of this input is `[T, N]`. Required.
+* **2**: `input_length` - 1D input tensor of type *T_IND* and of a shape `[N]`. The tensor must consist of values not greater than `T`. Lengths of input sequences `inputs[:,i,:]`. Required.
 
 * **3**: `labels` - 2D tensor with shape `[N, T]` of type *T_IND*. A sequence can be shorter than the size `T` of the tensor, all elements that do not code sequence classes are filled with -1. Required.
 
+* **4**: `blank_index` - Scalar. Set the class index to use for the blank label. Default value is `C-1`. Optional.
+
 **Output**
 
 * **1**: Output tensor with shape `[N]`, negative log of summed up probabilities for aligned paths. Type of elements is *T_F*.
@@ -78,7 +88,7 @@ CTCLoss = -\log \sum_{S} p(S)
 
 * *T_F*: any supported floating point type.
 
-* *T_IND*: any supported integer type.
+* *T_IND*: any supported signed integer type.
 
 **Example**
 
@@ -89,7 +99,7 @@ CTCLoss = -\log \sum_{S} p(S)
             <dim>20</dim>
             <dim>8</dim>
             <dim>128</dim>
-       </port>
+        </port>
         <port id="1">
             <dim>20</dim>
             <dim>8</dim>
@@ -97,12 +107,13 @@ CTCLoss = -\log \sum_{S} p(S)
         <port id="2">
             <dim>8</dim>
             <dim>20</dim>
-       </port>
+        </port>
+        <port id="3">
     </input>
     <output>
         <port id="0">
             <dim>8</dim>
-       </port>
+        </port>
     </output>
 </layer>
 ```