diff --git a/samples/dnn/tf_text_graph_ssd.py b/samples/dnn/tf_text_graph_ssd.py
new file mode 100644
index 0000000000..50fdc2ca3a
--- /dev/null
+++ b/samples/dnn/tf_text_graph_ssd.py
@@ -0,0 +1,255 @@
+# This file is a part of OpenCV project.
+# It is a subject to the license terms in the LICENSE file found in the top-level directory
+# of this distribution and at http://opencv.org/license.html.
+#
+# Copyright (C) 2018, Intel Corporation, all rights reserved.
+# Third party copyrights are property of their respective owners.
+#
+# Use this script to get the text graph representation (.pbtxt) of SSD-based
+# deep learning network trained in TensorFlow Object Detection API.
+# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
+# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
+import tensorflow as tf
+import argparse
+from math import sqrt
+from tensorflow.core.framework.node_def_pb2 import NodeDef
+from google.protobuf import text_format
+
+parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
+                                             'SSD model from TensorFlow Object Detection API. '
+                                             'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
+parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
+parser.add_argument('--output', required=True, help='Path to output text graph.')
+parser.add_argument('--num_classes', default=90, type=int, help='Number of trained classes.')
+parser.add_argument('--min_scale', default=0.2, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
+parser.add_argument('--max_scale', default=0.95, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
+parser.add_argument('--num_layers', default=6, type=int, help='Hyper-parameter of ssd_anchor_generator from config file.')
+parser.add_argument('--aspect_ratios', default=[1.0, 2.0, 0.5, 3.0, 0.333], type=float, nargs='+',
+                    help='Hyper-parameter of ssd_anchor_generator from config file.')
+args = parser.parse_args()
+
+# Nodes that should be kept.
+keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu6', 'Placeholder', 'FusedBatchNorm',
+           'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool']
+
+# Nodes attributes that could be removed because they are not used during import.
+unusedAttrs = ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
+               'Index', 'Tperm', 'is_training', 'Tpaddings']
+
+# Node with which prefixes should be removed
+prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Postprocessor/', 'Preprocessor/')
+
+# Read the graph.
+with tf.gfile.FastGFile(args.input, 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+
+def getUnconnectedNodes():
+    unconnected = []
+    for node in graph_def.node:
+        unconnected.append(node.name)
+        for inp in node.input:
+            if inp in unconnected:
+                unconnected.remove(inp)
+    return unconnected
+
+removedNodes = []
+
+# Detect unfused batch normalization nodes and fuse them.
+def fuse_batch_normalization():
+    pattern = ['Add', 'Rsqrt', 'Mul', 'Mul', 'Mul', 'Sub', 'Add']
+    candidates = []
+
+    for node in graph_def.node:
+        if node.op == pattern[len(candidates)]:
+            candidates.append(node)
+        else:
+            candidates = []
+
+        if len(candidates) == len(pattern):
+            inp = candidates[3].input[0]
+            gamma = candidates[2].input[1]
+            beta = candidates[5].input[0]
+            moving_mean = candidates[4].input[0]
+            moving_variance = candidates[0].input[0]
+
+            name = node.name
+            node.Clear()
+            node.name = name
+            node.op = 'FusedBatchNorm'
+            node.input.append(inp)
+            node.input.append(gamma)
+            node.input.append(beta)
+            node.input.append(moving_mean)
+            node.input.append(moving_variance)
+            text_format.Merge('f: 0.001', node.attr["epsilon"])
+
+            for candidate in candidates[:-1]:
+                graph_def.node.remove(candidate)
+            candidates = []
+
+fuse_batch_normalization()
+
+# Removes Identity nodes
+def removeIdentity():
+    identities = {}
+    for node in graph_def.node:
+        if node.op == 'Identity':
+            identities[node.name] = node.input[0]
+
+    for node in graph_def.node:
+        for i in range(len(node.input)):
+            if node.input[i] in identities:
+                node.input[i] = identities[node.input[i]]
+
+removeIdentity()
+
+# Remove extra nodes and attributes.
+for i in reversed(range(len(graph_def.node))):
+    op = graph_def.node[i].op
+    name = graph_def.node[i].name
+
+    if (not op in keepOps) or name.startswith(prefixesToRemove):
+        if op != 'Const':
+            removedNodes.append(name)
+
+        del graph_def.node[i]
+    else:
+        for attr in unusedAttrs:
+            if attr in graph_def.node[i].attr:
+                del graph_def.node[i].attr[attr]
+
+# Remove references to removed nodes except Const nodes.
+for node in graph_def.node:
+    for i in reversed(range(len(node.input))):
+        if node.input[i] in removedNodes:
+            del node.input[i]
+
+# Connect input node to the first layer
+assert(graph_def.node[0].op == 'Placeholder')
+# assert(graph_def.node[1].op == 'Conv2D')
+weights = graph_def.node[1].input[0]
+for i in range(len(graph_def.node[1].input)):
+    graph_def.node[1].input.pop()
+graph_def.node[1].input.append(graph_def.node[0].name)
+graph_def.node[1].input.append(weights)
+
+# Create SSD postprocessing head ###############################################
+
+# Concatenate predictions of classes, predictions of bounding boxes and proposals.
+
+concatAxis = NodeDef()
+concatAxis.name = 'concat/axis_flatten'
+concatAxis.op = 'Const'
+text_format.Merge(
+'tensor {'
+'  dtype: DT_INT32'
+'  tensor_shape { }'
+'  int_val: -1'
+'}', concatAxis.attr["value"])
+graph_def.node.extend([concatAxis])
+
+def addConcatNode(name, inputs):
+    concat = NodeDef()
+    concat.name = name
+    concat.op = 'ConcatV2'
+    for inp in inputs:
+        concat.input.append(inp)
+    concat.input.append(concatAxis.name)
+    graph_def.node.extend([concat])
+
+for label in ['ClassPredictor', 'BoxEncodingPredictor']:
+    concatInputs = []
+    for i in range(args.num_layers):
+        # Flatten predictions
+        flatten = NodeDef()
+        inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
+        flatten.input.append(inpName)
+        flatten.name = inpName + '/Flatten'
+        flatten.op = 'Flatten'
+
+        concatInputs.append(flatten.name)
+        graph_def.node.extend([flatten])
+    addConcatNode('%s/concat' % label, concatInputs)
+
+# Add layers that generate anchors (bounding boxes proposals).
+scales = [args.min_scale + (args.max_scale - args.min_scale) * i / (args.num_layers - 1)
+          for i in range(args.num_layers)] + [1.0]
+
+def tensorMsg(values):
+    msg = 'tensor { dtype: DT_FLOAT tensor_shape { dim { size: %d } }' % len(values)
+    for value in values:
+        msg += 'float_val: %f ' % value
+    return msg + '}'
+
+priorBoxes = []
+for i in range(args.num_layers):
+    priorBox = NodeDef()
+    priorBox.name = 'PriorBox_%d' % i
+    priorBox.op = 'PriorBox'
+    priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
+    priorBox.input.append(graph_def.node[0].name)  # image_tensor
+
+    text_format.Merge('b: false', priorBox.attr["flip"])
+    text_format.Merge('b: false', priorBox.attr["clip"])
+    text_format.Merge('b: true', priorBox.attr["normalized_bbox"])
+
+    if i == 0:
+        widths = [args.min_scale * 0.5, args.min_scale * sqrt(2.0), args.min_scale * sqrt(0.5)]
+        heights = [args.min_scale * 0.5, args.min_scale / sqrt(2.0), args.min_scale / sqrt(0.5)]
+    else:
+        widths = [scales[i] * sqrt(ar) for ar in args.aspect_ratios]
+        heights = [scales[i] / sqrt(ar) for ar in args.aspect_ratios]
+
+        widths += [sqrt(scales[i] * scales[i + 1])]
+        heights += [sqrt(scales[i] * scales[i + 1])]
+    text_format.Merge(tensorMsg(widths), priorBox.attr["width"])
+    text_format.Merge(tensorMsg(heights), priorBox.attr["height"])
+    text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
+
+    graph_def.node.extend([priorBox])
+    priorBoxes.append(priorBox.name)
+
+addConcatNode('PriorBox/concat', priorBoxes)
+
+# Sigmoid for classes predictions and DetectionOutput layer
+sigmoid = NodeDef()
+sigmoid.name = 'ClassPredictor/concat/sigmoid'
+sigmoid.op = 'Sigmoid'
+sigmoid.input.append('ClassPredictor/concat')
+graph_def.node.extend([sigmoid])
+
+detectionOut = NodeDef()
+detectionOut.name = 'detection_out'
+detectionOut.op = 'DetectionOutput'
+
+detectionOut.input.append('BoxEncodingPredictor/concat')
+detectionOut.input.append(sigmoid.name)
+detectionOut.input.append('PriorBox/concat')
+
+text_format.Merge('i: %d' % (args.num_classes + 1), detectionOut.attr['num_classes'])
+text_format.Merge('b: true', detectionOut.attr['share_location'])
+text_format.Merge('i: 0', detectionOut.attr['background_label_id'])
+text_format.Merge('f: 0.6', detectionOut.attr['nms_threshold'])
+text_format.Merge('i: 100', detectionOut.attr['top_k'])
+text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
+text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
+text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold'])
+text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
+
+graph_def.node.extend([detectionOut])
+
+while True:
+    unconnectedNodes = getUnconnectedNodes()
+    unconnectedNodes.remove(detectionOut.name)
+    if not unconnectedNodes:
+        break
+
+    for name in unconnectedNodes:
+        for i in range(len(graph_def.node)):
+            if graph_def.node[i].name == name:
+                del graph_def.node[i]
+                break
+
+# Save as text.
+tf.train.write_graph(graph_def, "", args.output, as_text=True)