diff --git a/src/arcaea_offline_ocr/ocr.py b/src/arcaea_offline_ocr/ocr.py
index b15f0d1..83a30b4 100644
--- a/src/arcaea_offline_ocr/ocr.py
+++ b/src/arcaea_offline_ocr/ocr.py
@@ -3,7 +3,6 @@ from typing import Optional, Sequence, Tuple
 
 import cv2
 import numpy as np
-from numpy.linalg import norm
 
 from .crop import crop_xywh
 from .types import Mat, cv2_ml_KNearest
@@ -142,28 +141,11 @@ def resize_fill_square(img: Mat, target: int = 20):
 
 
 def preprocess_hog(digit_rois):
-    # https://github.com/opencv/opencv/blob/f834736307c8328340aea48908484052170c9224/samples/python/digits.py
+    # https://learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial/
     samples = []
     for digit in digit_rois:
-        gx = cv2.Sobel(digit, cv2.CV_32F, 1, 0)
-        gy = cv2.Sobel(digit, cv2.CV_32F, 0, 1)
-        mag, ang = cv2.cartToPolar(gx, gy)
-        bin_n = 16
-        _bin = np.int32(bin_n * ang / (2 * np.pi))
-        bin_cells = _bin[:10, :10], _bin[10:, :10], _bin[:10, 10:], _bin[10:, 10:]
-        mag_cells = mag[:10, :10], mag[10:, :10], mag[:10, 10:], mag[10:, 10:]
-        hists = [
-            np.bincount(b.ravel(), m.ravel(), bin_n)
-            for b, m in zip(bin_cells, mag_cells)
-        ]
-        hist = np.hstack(hists)
-
-        # transform to Hellinger kernel
-        eps = 1e-7
-        hist /= hist.sum() + eps
-        hist = np.sqrt(hist)
-        hist /= norm(hist) + eps
-
+        hog = cv2.HOGDescriptor((20, 20), (10, 10), (5, 5), (10, 10), 9)
+        hist = hog.compute(digit)
         samples.append(hist)
     return np.float32(samples)