diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py
index 643c83b..650fa3d 100644
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@@ -414,7 +414,7 @@ class Eynollah:
             is_image_enhanced = False
 
         self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
-        image['img_res'] = img_res
+        image['img_res'] = img_res.astype(np.uint8)
         image['scale_y'] = 1.0 * img_res.shape[0] / img.shape[0]
         image['scale_x'] = 1.0 * img_res.shape[1] / img.shape[1]
         return is_image_enhanced, num_col, is_image_resized
@@ -447,7 +447,7 @@ class Eynollah:
             if is_enhancement:
                 seg = (label_p_pred * 255).astype(np.uint8)
             else:
-                seg = np.argmax(label_p_pred, axis=2)
+                seg = np.argmax(label_p_pred, axis=2).astype(np.uint8)
 
             if thresholding_for_artificial_class:
                 seg_mask_label(
@@ -460,7 +460,7 @@ class Eynollah:
                     seg, label_p_pred[:, :, heading_class] >= 0.2,
                     label=heading_class)
 
-            return resize_image(seg, img_h_page, img_w_page).astype(np.uint8)
+            return resize_image(seg, img_h_page, img_w_page)
 
         if img_h_page < img_height_model:
             img = resize_image(img, img_height_model, img.shape[1])
@@ -567,7 +567,7 @@ class Eynollah:
         if is_enhancement:
             seg = (prediction * 255).astype(np.uint8)
         else:
-            seg = np.argmax(prediction, axis=2)
+            seg = np.argmax(prediction, axis=2).astype(np.uint8)
         if thresholding_for_some_classes:
             seg_mask_label(
                 seg, prediction[:, :, 4] > 0.03,
@@ -616,9 +616,9 @@ class Eynollah:
             img = resize_image(img, img_height_model, img_width_model)
 
             label_p_pred = model.predict(img[np.newaxis], verbose=0)[0]
-            seg = np.argmax(label_p_pred, axis=2)
+            seg = np.argmax(label_p_pred, axis=2).astype(np.uint8)
 
-            prediction = resize_image(seg, img_h_page, img_w_page).astype(np.uint8)
+            prediction = resize_image(seg, img_h_page, img_w_page)
 
             if thresholding_for_artificial_class:
                 mask = resize_image(label_p_pred[:, :, artificial_class],
@@ -741,7 +741,7 @@ class Eynollah:
                     img_patch[:] = 0
 
         # decode
-        seg = np.argmax(prediction, axis=2)
+        seg = np.argmax(prediction, axis=2).astype(np.uint8)
         conf = prediction[tuple(np.indices(seg.shape)) + (seg,)]
         if thresholding_for_artificial_class:
             seg_art = prediction[:, :, artificial_class] >= threshold_art_class
@@ -1082,10 +1082,9 @@ class Eynollah:
         mask_seps_only = (prediction_regions == label_seps).astype('uint8')
         mask_tabs_only = prediction_tables
 
-        ##if num_col_classifier == 1 or num_col_classifier == 2:
-            ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
-            ##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
-        mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
+        # if num_col_classifier == 1 or num_col_classifier == 2:
+        #     mask_texts_only = cv2.morphologyEx(mask_texts_only, cv2.MORPH_OPEN, KERNEL, iterations=1)
+        mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2, 2), np.uint8), iterations=1)
 
         polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
         polygons_seplines = filter_contours_area_of_image(