diff --git a/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown b/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown
index 5fbbff27a32957f6b8d132cca95972e070038c81..1ef8443306f9b5b38bada068ef21cf1106981488 100644
--- a/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown
+++ b/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown
@@ -21,7 +21,6 @@ train_data, and next 250 samples as test_data. So let's prepare them first.
 @code{.py}
 import numpy as np
 import cv2 as cv
-from matplotlib import pyplot as plt
 
 img = cv.imread('digits.png')
 gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
@@ -89,7 +88,6 @@ alphabets directly.
 @code{.py}
 import cv2 as cv
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Load the data, converters convert the letter to a number
 data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',