diff --git a/.gitignore b/.gitignore
index bac3af5..1cf261d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 /pdfs/
+/results/
diff --git a/vidocp/table_parsing.py b/vidocp/table_parsing.py
index 88a8790..2301ac1 100644
--- a/vidocp/table_parsing.py
+++ b/vidocp/table_parsing.py
@@ -10,10 +10,8 @@ from vidocp.utils.display import show_mpl
 from vidocp.utils.draw import draw_rectangles
 from vidocp.utils.post_processing import xywh_to_vecs, xywh_to_vec_rect, adjacent1d, remove_isolated
 
-import matplotlib.pyplot as plt
 
 def add_external_contours(image, img):
-
     contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
 
     # contours = filter(partial(is_large_enough, min_area=5000000), contours)
@@ -24,46 +22,107 @@ def add_external_contours(image, img):
 
     return image
 
-def process_lines(img_bin_h, img_bin_v):
-    def draw_lines(lines, img_bin):
-        for line in lines:
+
+def process_lines(img_line_component):
+    def draw_lines(detected_lines, img_bin):
+        for line in detected_lines:
             for x1, y1, x2, y2 in line:
                 cv2.line(img_bin, (x1, y1), (x2, y2), (255, 255, 255), 6)
                 return img_bin
-    lines_h = cv2.HoughLinesP(img_bin_h, 1, np.pi / 180, 500, 500, 250)
-    draw_lines(lines_h, img_bin_h)
 
-    lines_v = cv2.HoughLinesP(img_bin_v, 0.7, np.pi / 180, 500, 500, 250)
-    draw_lines(lines_v,img_bin_v)
+    lines = cv2.HoughLines(img_line_component, 1, np.pi / 180, 500)
+    draw_lines(lines, lines)
+
+    return img_line_component
+
+# def isolate_vertical_and_horizontal_components(img_bin):
+#     line_min_width = 50
+#     kernel_h = np.ones((1, line_min_width), np.uint8)
+#     kernel_v = np.ones((line_min_width, 1), np.uint8)
+#
+#     img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
+#     img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
+#     show_mpl(img_bin_h | img_bin_v)
+#
+#     img_bin_h = apply_motion_blur(img_bin_h, 140, 0)
+#     img_bin_v = apply_motion_blur(img_bin_v, 140, 90)
+#     show_mpl(img_bin_h | img_bin_v)
+#
+#     th1, img_bin_h = cv2.threshold(img_bin_h, 95, 255, cv2.THRESH_BINARY)
+#     th1, img_bin_v = cv2.threshold(img_bin_v, 95, 255, cv2.THRESH_BINARY)
+#     show_mpl(img_bin_h | img_bin_v)
+#
+#     kernel_h = np.ones((1, 8), np.uint8)
+#     kernel_v = np.ones((8, 1), np.uint8)
+#     img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=4)
+#     img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=4)
+#
+#     img_bin_final = img_bin_h | img_bin_v
+#     show_mpl(img_bin_final)
+#     # th 130
+#     #th1, img_bin_final = cv2.threshold(img_bin_final, 90, 255, cv2.THRESH_BINARY)
+#     #show_mpl(img_bin_final)
+#     return img_bin_final
 
-    return img_bin_h, img_bin_v
 
 def isolate_vertical_and_horizontal_components(img_bin):
-
     line_min_width = 30
     kernel_h = np.ones((1, line_min_width), np.uint8)
     kernel_v = np.ones((line_min_width, 1), np.uint8)
 
     img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
     img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
+    show_mpl(img_bin_h | img_bin_v)
 
-    img_bin_h = cv2.dilate(img_bin_h, kernel_h, 1)
-    img_bin_v = cv2.dilate(img_bin_v, kernel_v, 1)
+    img_bin_h = apply_motion_blur(img_bin_h, 150, 0)
+    img_bin_v = apply_motion_blur(img_bin_v, 150, 90)
+    show_mpl(img_bin_h | img_bin_v)
+
+    th1, img_bin_h = cv2.threshold(img_bin_h, 70, 255, cv2.THRESH_BINARY)
+    th1, img_bin_v = cv2.threshold(img_bin_v, 70, 255, cv2.THRESH_BINARY)
+    show_mpl(img_bin_h | img_bin_v)
+
+    kernel_h = np.ones((1, 10), np.uint8)
+    kernel_v = np.ones((10, 1), np.uint8)
+    img_bin_h = cv2.erode(img_bin_h, kernel_h, iterations=1)
+    img_bin_v = cv2.erode(img_bin_v, kernel_v, iterations=1)
 
-    img_bin_h = apply_motion_blur(img_bin_h, 100, 0)
-    img_bin_v = apply_motion_blur(img_bin_v, 100, 90)
-    # img_bin_h, img_bin_v = process_lines(img_bin_h,img_bin_v)
     img_bin_final = img_bin_h | img_bin_v
-    kernel = np.ones((5, 5), np.uint8)
-    # img_bin_final = cv2.dilate(img_bin_final, kernel, 2)
-    th1, img_bin_final = cv2.threshold(img_bin_final, 10, 255, cv2.THRESH_BINARY)
     show_mpl(img_bin_final)
+    # th 130
+    # th1, img_bin_final = cv2.threshold(img_bin_final, 150, 255, cv2.THRESH_BINARY)
+    # show_mpl(img_bin_final)
     return img_bin_final
 
+# def isolate_vertical_and_horizontal_components(img_bin):
+#     line_min_width = 30
+#     kernel_h = np.ones((1, line_min_width), np.uint8)
+#     kernel_v = np.ones((line_min_width, 1), np.uint8)
+#
+#     img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
+#     img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
+#     show_mpl(img_bin_h | img_bin_v)
+#
+#     kernel_h = np.ones((1, 30), np.uint8)
+#     kernel_v = np.ones((30, 1), np.uint8)
+#     img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=1)
+#     img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=1)
+#     show_mpl(img_bin_h | img_bin_v)
+#
+#     img_bin_h = apply_motion_blur(img_bin_h, 100, 0)
+#     img_bin_v = apply_motion_blur(img_bin_v, 100, 90)
+#
+#     img_bin_final = img_bin_h | img_bin_v
+#     show_mpl(img_bin_final)
+#     # th 130
+#     th1, img_bin_final = cv2.threshold(img_bin_final, 125, 255, cv2.THRESH_BINARY)
+#     show_mpl(img_bin_final)
+#
+#     return img_bin_final
+
 
 # FIXME: does not work yet
 def has_table_shape(rects):
-
     assert isinstance(rects, list)
 
     points = list(chain(*map(xywh_to_vecs, rects)))
@@ -96,29 +155,24 @@ def has_table_shape(rects):
     )
 
 
-
 def apply_motion_blur(image, size, angle):
     k = np.zeros((size, size), dtype=np.float32)
-    k[ (size-1)// 2 , :] = np.ones(size, dtype=np.float32)
-    k = cv2.warpAffine(k, cv2.getRotationMatrix2D( (size / 2 -0.5 , size / 2 -0.5 ) , angle, 1.0), (size, size) )
-    k = k * ( 1.0 / np.sum(k) )
+    k[(size - 1) // 2, :] = np.ones(size, dtype=np.float32)
+    k = cv2.warpAffine(k, cv2.getRotationMatrix2D((size / 2 - 0.5, size / 2 - 0.5), angle, 1.0), (size, size))
+    k = k * (1.0 / np.sum(k))
     return cv2.filter2D(image, -1, k)
 
 
 def parse_table(image: np.array):
     def is_large_enough(stat):
         x1, y1, w, h, area = stat
-        #  was set too high (3000): Boxes in a Table can be smaller. example: a column titled "No." This cell has approximatly an area of 500 px based on 11pt letters
-        #  with extra condition for the length of height and width weirdly narrow rectangles can be filtered
         return area > 500 and w > 35 and h > 15
 
     gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    blur_gray_scale = cv2.GaussianBlur(gray_scale, (5, 5), 1, borderType=cv2.BORDER_REPLICATE)
-    th1, img_bin = cv2.threshold(blur_gray_scale, 195, 255, cv2.THRESH_BINARY)
-    show_mpl(img_bin)
-    # changed threshold value from 150 to 195 because of a shaded edgecase table
-    # th1, img_bin = cv2.threshold(gray_scale, 195, 255, cv2.THRESH_BINARY)
+    # blur_gray_scale = cv2.GaussianBlur(gray_scale, (5, 5), 1, borderType=cv2.BORDER_REPLICATE)
+    th1, img_bin = cv2.threshold(gray_scale, 195, 255, cv2.THRESH_BINARY)
     img_bin = ~img_bin
+    show_mpl(img_bin)
 
     img_bin = isolate_vertical_and_horizontal_components(img_bin)
     img_bin_final = add_external_contours(img_bin, img_bin)
@@ -134,13 +188,10 @@ def parse_table(image: np.array):
     # if not has_table_shape(rects):
     #     return False
 
-
-
     return rects
 
 
 def annotate_tables_in_pdf(pdf_path, page_index=1):
-
     page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
     page = np.array(page)