diff --git a/codes/ecg-image-generator/README.md b/codes/ecg-image-generator/README.md
index 5b52599..e860c8b 100644
--- a/codes/ecg-image-generator/README.md
+++ b/codes/ecg-image-generator/README.md
@@ -79,15 +79,6 @@ The basic mode of the tool creates ECG images without distortions. The mode of o
      python gen_ecg_images_from_data_batch.py -i <path_to_input_directory> -o <path_to_output_directory> -se 10 --store_text_bounding_box --bbox --random_add_header 0.8 --random_dc 0.5
      ```
 
-     - Below are sample synthetic ECG images generated from sample records of the [PhysioNet PTB-XL](https://physionet.org/content/ptb-xl/) dataset using the script above.
-
-          |                           |                             |
-          | ----------------------------------- | ----------------------------------- |
-          | ![Sythentic ECG image](SampleData/PTB_XL_op/box_plots/00001_lr-0.png) | ![GT Image](SampleData/PTB_XL_op/box_plots/00004_lr-0.png) |
-
-     *Note*: The red and green boxes here have been plotted from the corresponding text files for each lead and lead-name text.
-
-
 ## Adding distortions to the synethic images
 - ### Text distortions
      Scanned ECG images often contain handwritten notes by physicians, sometimes overlapping the ECG traces. Our toolkit simulates this by using a dictionary of relevant keywords, which are randomly placed on the ECG images. We gathered medical texts related to ECG and cardiovascular diseases and employed Natural Language Processing (NLP) models to extract biomedical phrases and keywords. These were transformed into handwritten-style images using pretrained models and overlaid on the ECG images.