diff --git a/tests/common.py b/tests/common.py
index 24cab7052..dec0b6e06 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -207,7 +207,7 @@ def requires_custom_ops(message=""):
     """ Skip until custom ops framework is on PyPI. """
     reason = _append_message("test needs custom ops framework", message)
     try:
-        import ortcustomops  #pylint: disable=import-outside-toplevel,unused-import
+        import onnxruntime_customops  #pylint: disable=import-outside-toplevel,unused-import
         can_import = True
     except ModuleNotFoundError:
         can_import = False
diff --git a/tests/test_backend.py b/tests/test_backend.py
index 80f950288..f7127c6b4 100644
--- a/tests/test_backend.py
+++ b/tests/test_backend.py
@@ -2834,6 +2834,36 @@ def func(x):
                 return tf.identity(res, name=_TFOUTPUT)
             self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @check_tf_min_version("1.14", "tf.strings.lower")
+    @check_opset_min_version(10, "StringNormalizer")
+    def test_string_lower(self):
+        text_val1 = np.array([["a", "Test 1 2 3", "♠♣"], ["Hi there", "test test", "♥♦"]], dtype=np.str)
+        def func(text1):
+            x = tf.strings.lower(text1)
+            x_ = tf.identity(x, name=_TFOUTPUT)
+            return x_
+        self._run_test_case(func, [_OUTPUT], {_INPUT: text_val1})
+
+    @check_tf_min_version("1.14", "tf.strings.lower")
+    @check_opset_min_version(10, "StringNormalizer")
+    def test_string_lower_flat(self):
+        text_val1 = np.array(["a", "Test 1 2 3", "♠♣", "Hi there", "test test", "♥♦"], dtype=np.str)
+        def func(text1):
+            x = tf.strings.lower(text1)
+            x_ = tf.identity(x, name=_TFOUTPUT)
+            return x_
+        self._run_test_case(func, [_OUTPUT], {_INPUT: text_val1})
+
+    @check_tf_min_version("1.14", "tf.strings.lower")
+    @check_opset_min_version(10, "StringNormalizer")
+    def test_string_upper(self):
+        text_val1 = np.array([["a", "Test 1 2 3", "♠♣"], ["Hi there", "test test", "♥♦"]], dtype=np.str)
+        def func(text1):
+            x = tf.strings.upper(text1)
+            x_ = tf.identity(x, name=_TFOUTPUT)
+            return x_
+        self._run_test_case(func, [_OUTPUT], {_INPUT: text_val1})
+
     @check_opset_min_version(6, "cast")
     def test_shape_int32(self):
         x_val = np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]], dtype=np.float32)
diff --git a/tests/test_string_ops.py b/tests/test_string_ops.py
index d4f83e714..096a5ea6d 100644
--- a/tests/test_string_ops.py
+++ b/tests/test_string_ops.py
@@ -117,7 +117,7 @@ def _run_test_case(self, func, output_names_with_port, feed_dict, **kwargs):
 
     def run_onnxruntime(self, model_path, inputs, output_names):
         """Run test against onnxruntime backend."""
-        from ortcustomops import get_library_path
+        from onnxruntime_customops import get_library_path
         import onnxruntime as rt
         opt = rt.SessionOptions()
         opt.register_custom_ops_library(get_library_path())
diff --git a/tf2onnx/custom_opsets/string_ops.py b/tf2onnx/custom_opsets/string_ops.py
index 34d7cc661..341920383 100644
--- a/tf2onnx/custom_opsets/string_ops.py
+++ b/tf2onnx/custom_opsets/string_ops.py
@@ -121,6 +121,28 @@ def version_1(cls, ctx, node, **kwargs):
             ctx.copy_shape(output_name, not_node.output[0])
             ctx.copy_dtype(output_name, not_node.output[0])
 
+@tf_op(["StringLower", "StringUpper"])
+class StringLower:
+    @classmethod
+    def version_10(cls, ctx, node, **kwargs):
+        if node.type == "StringLower":
+            case_action = "LOWER"
+        else:
+            case_action = "UPPER"
+        node.type = "StringNormalizer"
+        str_input = node.input[0]
+        rank = ctx.get_rank(node.input[0])
+        shape = ctx.get_shape(node.input[0])
+        if rank != 1:
+            ctx.insert_new_node_on_input(node, "Flatten", node.input[0], axis=0)
+        node.set_attr("case_change_action", case_action)
+        if rank != 1:
+            if shape is None or -1 in shape:
+                new_shape = ctx.make_node("Shape", [str_input]).output[0]
+            else:
+                new_shape = ctx.make_const(utils.make_name("shape"), np.array(shape, np.int64)).output[0]
+            ctx.insert_new_node_on_output("Reshape", node.output[0], inputs=[node.output[0], new_shape])
+
 @tf_op("SentencepieceOp", domain=constants.CONTRIB_OPS_DOMAIN)
 class SentencepieceOp:
     @classmethod