diff --git a/OCR/image-alignment/deskew.py b/OCR/image-alignment/deskew.py
index 37fd2144..ec6c642e 100755
--- a/OCR/image-alignment/deskew.py
+++ b/OCR/image-alignment/deskew.py
@@ -12,35 +12,37 @@
 import cv2 as cv
 
 
-def order_points(inn: np.ndarray) -> np.ndarray:
-    "Reorder points such that the points go in order from top left, top right, bottom right, and bottom left."
-    inn = inn.reshape(4, 2)
-    out = np.zeros([4, 2]).astype(np.float32)
-    s = inn.sum(axis=1)
-    out[0] = inn[np.argmin(s)]
-    out[2] = inn[np.argmax(s)]
-    diff = np.diff(inn, axis=1)
-    out[1] = inn[np.argmin(diff)]
-    out[3] = inn[np.argmax(diff)]
-    return out
-
-
-def dewarp(img: np.ndarray) -> np.ndarray:
+def order_points(quadrilateral: np.ndarray) -> np.ndarray:
+    "Reorder points from a 4x2 input array representing the vertices of a quadrilateral, such that the coordinates of each vertex are arranged in order from top left, top right, bottom right, and bottom left."
+    quadrilateral = quadrilateral.reshape(4, 2)
+    output_quad = np.zeros([4, 2]).astype(np.float32)
+    s = quadrilateral.sum(axis=1)
+    output_quad[0] = quadrilateral[np.argmin(s)]
+    output_quad[2] = quadrilateral[np.argmax(s)]
+    diff = np.diff(quadrilateral, axis=1)
+    output_quad[1] = quadrilateral[np.argmin(diff)]
+    output_quad[3] = quadrilateral[np.argmax(diff)]
+    return output_quad
+
+
+def dewarp(image: np.ndarray) -> np.ndarray:
     # compute contours for an image and find the biggest one by area
-    cnts, hier = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[-2:]
+    _, contours, _ = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
     biggest_contour = functools.reduce(
-        lambda a, b: b if cv.contourArea(a) < cv.contourArea(b) else a, cnts
+        lambda a, b: b if cv.contourArea(a) < cv.contourArea(b) else a, contours
     )
 
     # simplify to a polygon with (hopefully four) vertices
-    peri = cv.arcLength(biggest_contour, True)
-    approx = cv.approxPolyDP(biggest_contour, 0.01 * peri, True)
+    perimeter = cv.arcLength(biggest_contour, True)
+    approximated = cv.approxPolyDP(biggest_contour, 0.01 * perimeter, True)
 
-    h, w = img.shape
-    dest = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.float32)
+    height, width = image.shape
+    destination = np.array(
+        [[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32
+    )
 
-    M = cv.getPerspectiveTransform(order_points(approx), dest)
-    return cv.warpPerspective(img, M, (w, h))
+    M = cv.getPerspectiveTransform(order_points(approximated), destination)
+    return cv.warpPerspective(image, M, (width, height))
 
 
 def parse_args() -> argparse.Namespace:
diff --git a/OCR/image-alignment/perspective-transform.py b/OCR/image-alignment/perspective-transform.py
index 15a14fa5..1492786e 100755
--- a/OCR/image-alignment/perspective-transform.py
+++ b/OCR/image-alignment/perspective-transform.py
@@ -26,7 +26,7 @@ def parse_args():
     return p.parse_args()
 
 
-def make_tform(distortion_scale: float) -> object:
+def make_transform(distortion_scale: float) -> object:
     return transforms.Compose(
         [
             transforms.RandomPerspective(distortion_scale=distortion_scale, p=1),
@@ -43,7 +43,7 @@ def main():
     img = Image.open(args.image)
 
     with concurrent.futures.ProcessPoolExecutor() as executor:
-        futures = [executor.submit(make_tform(ii / 10), img) for ii in range(1, 10)]
+        futures = [executor.submit(make_transform(ii / 10), img) for ii in range(1, 10)]
         for idx, result in enumerate(concurrent.futures.as_completed(futures)):
             new_name = args.output_dir / (
                 args.image.stem + f"-{idx + 1}" + args.image.suffix