diff --git a/resources/benchmarks/table_benchmark_on_generated_data.json b/resources/benchmarks/table_benchmark_on_generated_data.json
index 130bcd28..95bd4100 100644
--- a/resources/benchmarks/table_benchmark_on_generated_data.json
+++ b/resources/benchmarks/table_benchmark_on_generated_data.json
@@ -1,506 +1,506 @@
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+    "0IKFXKSQ9OA3OCRGQBZFI.png": 0.1855624121249121,
+    "1C70JEJWPOAT1S8RUWCVB.png": 1.0,
+    "0A7ZU26KX6C0LG0D3T3ZS.png": 0.9135802469135803,
+    "0BL58Q9DLPBQF73ROGFDX.png": 0.9308702743618086,
+    "0UQWQMAYVXUFY65GH4ION.png": 0.8006377881377882,
+    "0R77TU5P7A0F1YTLIGSOA.png": 0.9922839506172839,
+    "0Q740R8QE6ZAF034ZMGQG.png": 0.8527251768699138,
+    "1BG5K95UCWQ3JXWC501XA.png": 0.9457231040564374,
+    "1CPW27F5C8I03UQBVBL2Y.png": 0.9566377342345018
   }
 }
\ No newline at end of file
diff --git a/resources/benchmarks/table_benchmark_on_our_data.json b/resources/benchmarks/table_benchmark_on_our_data.json
new file mode 100644
index 00000000..704bd424
--- /dev/null
+++ b/resources/benchmarks/table_benchmark_on_our_data.json
@@ -0,0 +1,37 @@
+{
+  "mode_metric_structure_only": false,
+  "mean": 0.9675821602750511,
+  "images": {
+    "example_with_table0_0.png": 0.9525583036909738,
+    "example_with_table0_1.png": 0.9264351862896008,
+    "example_with_table6.png": 0.989010989010989,
+    "example_with_table4.jpg": 0.908436211832951,
+    "example_with_table17.jpg": 0.8078952936402488,
+    "example_with_table_hor_vert_union.png": 0.9896091617933723,
+    "example_with_table1.png": 0.9781560283687943,
+    "example_with_table_horizontal_union.jpg": 0.9925757575757576,
+    "example_with_table3.png": 0.9778008866078716,
+    "example_with_table5.png": 0.9458965482130129,
+    "img_00029.png": 1.0,
+    "img_00027.png": 0.9031857944901424,
+    "im_1_2.png": 1.0,
+    "im_1_3.png": 0.986863711001642,
+    "img_00054.png": 0.9914529914529915,
+    "img_00471.png": 0.9991017291713452,
+    "img_00028.png": 0.9620735785953177,
+    "im_1_4.png": 0.9416131827145962,
+    "img_00449.png": 0.9521500666916612,
+    "img_00025.png": 1.0,
+    "img_00479.png": 0.96875,
+    "im_1_1.png": 0.9978354978354979,
+    "img_00026.png": 0.9998530708198649,
+    "img_00317.png": 1.0,
+    "img_00030.png": 0.9487179487179487,
+    "img_00047.png": 1.0,
+    "img_00410.png": 1.0,
+    "img_00162.png": 1.0,
+    "img_00035.png": 0.9669117647058824,
+    "img_00478.png": 1.0,
+    "img_00461.png": 0.9081632653061225
+  }
+}
\ No newline at end of file
diff --git a/scripts/benchmark_table/benchmark_table.py b/scripts/benchmark_table/benchmark_table.py
index 8a52048d..ace74761 100644
--- a/scripts/benchmark_table/benchmark_table.py
+++ b/scripts/benchmark_table/benchmark_table.py
@@ -84,21 +84,22 @@ def benchmark_on_our_data() -> dict:
     path_images = data_dir / "images"
     path_gt = data_dir / "gt.json"
     path_pred = data_dir / "pred.json"
-    download_dataset(data_dir, name_zip="benchmark_table_data.zip", url="https://at.ispras.ru/owncloud/index.php/s/Xaf4OyHj6xN2RHH/download")
+    result = dict()
+    download_dataset(data_dir, name_zip="benchmark_table_data.zip", url="https://at.ispras.ru/owncloud/index.php/s/nYgwbhVk5SpvD3z/download")
 
     mode_metric_structure_only = False
 
     with open(path_gt, "r") as fp:
         gt_json = json.load(fp)
+
     """
-    Creating base html (based on method predictions for future labeling)
-    path_images = data_dir / "images_tmp"
-    pred_json = prediction("gt_tmp.json", path_images)
+    # Creating base html (based on method predictions for future labeling)
+    pred_json = prediction(data_dir / "gt_generated.json", path_images)
     """
+
     pred_json = prediction(path_pred, path_images)
     scores = call_metric(pred_json=pred_json, true_json=gt_json, structure_only=mode_metric_structure_only)
 
-    result = dict()
     result["mode_metric_structure_only"] = mode_metric_structure_only
     result["mean"] = np.mean([score for score in scores.values()])
     result["images"] = scores
@@ -123,7 +124,7 @@ def benchmark_on_generated_table() -> dict:
     download_dataset(data_dir,
                      name_zip="benchmark_table_data_generated_500_tables_category_1.zip",
                      url="https://at.ispras.ru/owncloud/index.php/s/gItWxupnF2pve6B/download")
-    mode_metric_structure_only = True
+    mode_metric_structure_only = False
 
     # make common ground-truth file
     common_gt_json = {}
@@ -143,9 +144,9 @@ def benchmark_on_generated_table() -> dict:
 
     # calculate metrics
     path_pred = data_dir / "pred.json"
-
     pred_json = prediction(path_pred, path_images)
-    scores = call_metric(pred_json=pred_json, true_json=common_gt_json, structure_only=mode_metric_structure_only, ignore_nodes=["span", "style", "head", "h4"])
+    scores = call_metric(pred_json=pred_json, true_json=common_gt_json, structure_only=mode_metric_structure_only,
+                         ignore_nodes=["span", "style", "head", "h4", "tbody"])
 
     result = dict()
     result["mode_metric_structure_only"] = mode_metric_structure_only
diff --git a/scripts/benchmark_table/metric.py b/scripts/benchmark_table/metric.py
index 28c46b5b..9bd107ac 100644
--- a/scripts/benchmark_table/metric.py
+++ b/scripts/benchmark_table/metric.py
@@ -8,7 +8,6 @@
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 # Apache 2.0 License for more details.
-
 # Source: https://github.com/ibm-aur-nlp/PubTabNet
 
 from collections import deque
@@ -55,6 +54,8 @@ def normalized_distance(self, *sequences) -> float:  # noqa
         """
         Get distance from 0 to 1
         """
+        if self.maximum(*sequences) == 0:
+            return 0
         return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
 
     def rename(self, node1: TableTree, node2: TableTree) -> float:
@@ -67,7 +68,7 @@ def rename(self, node1: TableTree, node2: TableTree) -> float:
             if not node1.visible or not node2.visible:
                 return 0.
             if node1.content or node2.content:
-                return self.normalized_distance(node1.content, node2.content)
+                return self.normalized_distance("".join(node1.content).strip(), "".join(node2.content).strip())
         return 0.