diff --git a/.gitignore b/.gitignore
index 79e3b1b5455702cd2d86be384f3da8c7e0d69f41..42de8ba8a80be8ea44b9e0ade74f8dd2a5a0490b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+.idea
 .ipynb_checkpoints
 __pycache__
 .venv
diff --git a/output/Laufon_009_preprocessed.pdf b/output/Laufon_009_preprocessed.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..1e11809e2e53e335cee07e9f381513417bf44a59
Binary files /dev/null and b/output/Laufon_009_preprocessed.pdf differ
diff --git a/output/Zeitungsausschnitt_preprocessed.pdf b/output/Zeitungsausschnitt_preprocessed.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f82b8d7c84f95ffdcce2170b787537156db84659
Binary files /dev/null and b/output/Zeitungsausschnitt_preprocessed.pdf differ
diff --git a/preprocessing.py b/preprocessing.py
index a3344e138ec2c4681622ed2653326ad5b5385aff..4864a74386fe3e0f751400c57e8f15ec8ce99024 100644
--- a/preprocessing.py
+++ b/preprocessing.py
@@ -3,14 +3,12 @@
 # desc: Provides functions to preprocess image to enhcance tesseract reults, currently applied:
 #       - deskew image (code adapted from https://docs.opencv.org/4.7.0/d1/dee/tutorial_introduction_to_pca.html)
 #       - crop image border regions
-#       - upscale image
 
 import os
 import cv2 as cv
 import numpy as np
 import matplotlib.pyplot as plt
 from math import atan2, cos, sin, sqrt, pi
-# import editdistance
 from IPython.display import HTML, Image, display
 
 
@@ -302,9 +300,10 @@ def preprocess_pipeline(image, debug=False, debug_path=None):
     img_rot, img_angle = deskew_image_pca(image, debug=debug, debug_path=debug_path)
     img_bb = get_cropping_corner_points(img_rot, threshold_border=0.05, debug=debug, debug_path=debug_path)
     img_crop = crop_bb_from_img(img_rot, img_bb, margin=15)
+    # Exclude upscaling for this demo
     # img_upscld = upscale_image(img_crop, scaling=2)
     img_res = cv.cvtColor(img_crop, cv.COLOR_BGR2GRAY)
-    # Following makes things gloablly worse, so I excluded these steps
+    # Optional other preprocessing steps
     # img_erode = cv.erode(img_gray, cv.getStructuringElement(cv.MORPH_CROSS, (3, 3)), iterations=1)
     # img_blur = cv.GaussianBlur(img_bright_contrast, (3, 3), 2)
     # img_sharp = unsharp_mask(img_gray, kernel_size=(3, 3), sigma=1.5, amount=5.0, threshold=50)
@@ -314,67 +313,16 @@ def preprocess_pipeline(image, debug=False, debug_path=None):
 
 if __name__ == '__main__':
     images = [
-        '/home/simon/Downloads/Ruthenica_sample/tesseract_output/00000001.jpg',
-        '/home/simon/Downloads/Ruthenica_sample/tesseract_output/00000003.jpg',
-        '/home/simon/Downloads/Ruthenica_sample/tesseract_output/00000004.jpg'
+        'img/Laufon_009.jpg',
+        'img/Zeitungsausschnitt.jpg'
     ]
 
-    # images = [
-    #     'img/test.jpg',
-    #     'img/Laufon_009.jpg',
-    #     'img/Laufon_012.jpg',
-    #     'img/Laufon_013.jpg',
-    #     'img/Laufon_055.jpg',
-    #     'img/Laufon_094.jpg',
-    #     'img/Laufon_110.jpg',
-    #     'img/Dresden_008.jpg',
-    #     'img/Dresden_020.jpg',
-    #     'img/Dresden_037.jpg',
-    #     'img/Dresden_063.jpg',
-    #     'img/Dresden_094.jpg',
-    #     'img/Dresden_132.jpg',
-    #     'img/00002_1003102D_00000021.jpg',
-    #     'img/00005_10026184_00000014.jpg',
-    #     'img/00063_10029F7D_00000022.jpg'
-    # ]
-    # measured_angles = [-0.65, 0.1, -1.3, -0.4, 0.85, 1.2,
-    #                    -0.1, -0.7, 0.5, -0.4, -0.7, -1.3,
-    #                    0.5, 0.3, -0.7]
-    # analyse_deskew_method(images, measured_angles, debug=False)
-    # ground_truth_fps = [
-    #     'ground_truths/GT_Laufon_009.txt',
-    #     'ground_truths/GT_Laufon_012.txt',
-    #     'ground_truths/GT_Laufon_013.txt',
-    #     'ground_truths/GT_Laufon_055.txt',
-    #     'ground_truths/GT_Laufon_094.txt',
-    #     'ground_truths/GT_Laufon_110.txt',
-    #     'ground_truths/GT_Dresden_008.txt',
-    #     'ground_truths/GT_Dresden_020.txt',
-    #     'ground_truths/GT_Dresden_037.txt',
-    #     'ground_truths/GT_Dresden_063.txt',
-    #     'ground_truths/GT_Dresden_094.txt',
-    #     'ground_truths/GT_Dresden_132.txt',
-    #     'ground_truths/GT_00002_1003102D_00000021.txt',
-    #     'ground_truths/GT_00005_10026184_00000014.txt',
-    #     'ground_truths/GT_00063_10029F7D_00000022.txt'
-    # ]
-
-    # ground_truths = [open(fp).read() for fp in ground_truth_fps]
-    # levenshtein_distances = []
-
     for img_path in images:
 
         img_path_preprocessed = f'{img_path.replace(".jpg", "")}_preprocessed.jpg'
         ocred_path_base_preprocessed = img_path_preprocessed.replace('.jpg', '')
-
         print(f'Processing image {img_path}')
         input_image = cv.imread(img_path)
-        preprocessed_img = preprocess_pipeline(input_image, debug=True)
-        # cv.imshow('Preprocessed image, rotated, cropped and resized', preprocessed_img)
-        # cv.waitKey()
-
+        preprocessed_img = preprocess_pipeline(input_image, debug=True)        
         cv.imwrite(img_path_preprocessed, preprocessed_img)
         os.system(f'tesseract -l ukr --dpi 300 --psm 4 {img_path_preprocessed} {ocred_path_base_preprocessed} txt')
-
-    # print('Levenshtein distances are:', levenshtein_distances)
-    # print('L2 norm is:', np.linalg.norm(levenshtein_distances))