diff --git a/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst b/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
index de4e585d8c..29e8b98571 100644
--- a/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
+++ b/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
@@ -133,7 +133,7 @@ Finds contours in a binary image.
 
 .. ocv:pyoldfunction:: cv.FindContours(image, storage, mode=CV_RETR_LIST, method=CV_CHAIN_APPROX_SIMPLE, offset=(0, 0)) -> contours
 
-    :param image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as  ``binary`` . You can use  :ocv:func:`compare` ,  :ocv:func:`inRange` ,  :ocv:func:`threshold` ,  :ocv:func:`adaptiveThreshold` ,  :ocv:func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the  ``image``  while extracting the contours.
+    :param image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as  ``binary`` . You can use  :ocv:func:`compare` ,  :ocv:func:`inRange` ,  :ocv:func:`threshold` ,  :ocv:func:`adaptiveThreshold` ,  :ocv:func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the  ``image``  while extracting the contours. If mode equals to ``CV_RETR_CCOMP`` or ``CV_RETR_FLOODFILL``, the input can also be a 32-bit integer image of labels (``CV_32SC1``).
 
     :param contours: Detected contours. Each contour is stored as a vector of points.