确认。详细代码如下：
import cv2
o1 = cv2.imread('cs1.bmp')
o2 = cv2.imread('cs2.bmp')
o3 = cv2.imread('cc.bmp')
gray1 = cv2.cvtColor(o1,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(o2,cv2.COLOR_BGR2GRAY)
gray3 = cv2.cvtColor(o3,cv2.COLOR_BGR2GRAY)
ret, binary1 = cv2.threshold(gray1,127,255,cv2.THRESH_BINARY)
ret, binary2 = cv2.threshold(gray2,127,255,cv2.THRESH_BINARY)
ret, binary3 = cv2.threshold(gray3,127,255,cv2.THRESH_BINARY)
contours1, hierarchy = cv2.findContours(binary1,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_**)
contours2, hierarchy = cv2.findContours(binary2,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_**)
contours3, hierarchy = cv2.findContours(binary3,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_**)
cnt1 = contours1[0]
cnt2 = contours2[0]
cnt3 = contours3[0]
ret0 = cv2.matchShapes(cnt1,cnt1,1,0.0)
ret1 = cv2.matchShapes(cnt1,cnt2,1,0.0)
ret2 = cv2.matchShapes(cnt1,cnt3,1,0.0)
print("o1.shape=",o1.shape)
print("o2.shape=",o2.shape)
print("o3.shape=",o3.shape)
print("相同图像的matchShape=",ret0)
print("相似图像的matchShape=",ret1)
print("不相似图像的matchShape=",ret2)
cv2.imshow("original1",o1)
cv2.imshow("original2",o2)
cv2.imshow("original3",o3)
cv2.waitKey()
cv2.destroyAllWindows()

我们这里关注的是识别原理，没有达到百分之百的准确率。
提高准确率，可以从如下几个方面着手：
1.提高样本数量，让更多的手写数字参与训练。
2.让训练的样本，与测试的样本更接近。

确认。
您好，感谢您的勘误。祝您进步！