diff --git a/doc/examples/plot_thresholding.py b/doc/examples/plot_thresholding.py
index e860f243..a0f7eb08 100644
--- a/doc/examples/plot_thresholding.py
+++ b/doc/examples/plot_thresholding.py
@@ -10,8 +10,8 @@ calculates an "optimal" threshold (marked by a red line in the histogram below)
 by maximizing the variance between two classes of pixels, which are separated by
 the threshold. Equivalently, this threshold minimizes the intra-class variance.
 
-Additionnally an adaptive thresholding is applied. Also known as local or
-dynamic thresholding where the the threshold value is the weighted mean for the
+Additionally an adaptive thresholding is applied. Also known as local or
+dynamic thresholding where the threshold value is the weighted mean for the
 local neighborhood of a pixel subtracted by a constant.
 
 .. [1] http://en.wikipedia.org/wiki/Otsu's_method
diff --git a/skimage/filter/thresholding.py b/skimage/filter/thresholding.py
index 931aeaa2..d0d95a4a 100644
--- a/skimage/filter/thresholding.py
+++ b/skimage/filter/thresholding.py
@@ -10,8 +10,8 @@ __all__ = ['threshold_otsu', 'adaptive_threshold']
 def adaptive_threshold(image, block_size, offset, method='gaussian'):
     """Applies an adaptive threshold to an array.
 
-    Also known as local or dynamic thresholding where the the threshold value is
-    the weighted mean for the local neighborhood of a pixel subtracted by a
+    Also known as local or dynamic thresholding where the threshold value is the
+    weighted mean for the local neighborhood of a pixel subtracted by a
     constant.
 
     Parameters