diff --git a/samples/python2/digits.py b/samples/python2/digits.py
index 38a95d67b..88e9fb8ad 100644
--- a/samples/python2/digits.py
+++ b/samples/python2/digits.py
@@ -124,5 +124,7 @@ if __name__ == '__main__':
     model.train(samples_train, labels_train)
     vis = evaluate_model(model, digits_test, samples_test, labels_test)
     cv2.imshow('SVM test', vis)
+    print 'saving SVM as "digits_svm.dat"...'
+    model.save('digits_svm.dat')
 
     cv2.waitKey(0)
diff --git a/samples/python2/digits_video.py b/samples/python2/digits_video.py
new file mode 100644
index 000000000..9b17bfacc
--- /dev/null
+++ b/samples/python2/digits_video.py
@@ -0,0 +1,63 @@
+import numpy as np
+import cv2
+#import video
+import digits
+from common import mosaic
+
+#cap = video.create_capture()
+cap = cv2.VideoCapture(0)
+
+model = digits.SVM()
+model.load('digits_svm.dat')
+
+SZ = 20
+
+while True:
+    ret, frame = cap.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
+    bin = cv2.medianBlur(bin, 3)
+    contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+    boxes = []
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        if h < 20 or h > 60 or 1.2*h < w:
+            continue
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
+        sub = bin[y:,x:][:h,:w]
+        #sub = ~cv2.equalizeHist(sub)
+        #_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
+
+        s = 1.1*h/SZ
+        m = cv2.moments(sub)
+        m00 = m['m00']
+        if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
+            continue
+
+        #frame[y:,x:][:h,:w] = sub[...,np.newaxis]
+        c1 = np.float32([m['m10'], m['m01']]) / m00
+        c0 = np.float32([SZ/2, SZ/2])
+        t = c1 - s*c0
+        A = np.zeros((2, 3), np.float32)
+        A[:,:2] = np.eye(2)*2
+        A[:,2] = t
+        sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
+        sub1 = digits.deskew(sub1)
+        sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0
+        digit = model.predict(sample)[0]
+
+        cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
+
+        boxes.append(sub1)
+
+
+    if len(boxes) > 0:
+        cv2.imshow('box', mosaic(10, boxes))
+        
+
+    cv2.imshow('frame', frame)
+    cv2.imshow('bin', bin)
+    if cv2.waitKey(1) == 27:
+        break