diff --git a/Article/method.tex b/Article/method.tex
index d38d22dea545b60d6b82b71a20169930f11b8c4d..3dbad38d6872bed954f3508b68b85ad5ebe757d9 100755
--- a/Article/method.tex
+++ b/Article/method.tex
@@ -72,7 +72,7 @@ from the scan strip (\RefFig{fig:escape} a).
 A second search is then run using another directional scan aligned
 on the detected segment (\RefFig{fig:escape} b).
 However, even in case of a correct detection, the estimated orientation
-of the segment is subject to the numerization rounding,
+of the segment is subject to the numerization rounding or outliers,
 and the longer the real segment to detect, the higher the probability to
 fail again on a blurred segment escape from the directional scan.
 
@@ -116,7 +116,7 @@ thus producing a useless computational cost.
 
 Here the proposed solution is to dynamically align the scan direction on
 the blurred segment one all along the expansion stage.
-At each iteration $i$, the scan strip is aligned on the direction of the
+At each iteration $i$ of the expansion, the scan strip is aligned on the direction of the
 blurred segment $\mathcal{B}_{i-1}$ computed at previous iteration $i-1$.
 More generally, an adaptive directional scan $ADS$ is defined by:
 \begin{equation}
@@ -276,7 +276,7 @@ Another option, called {\it multi-detection} (Algorithm 1), allows the
 detection of all the segments crossed by the input stroke $AB$.
 In order to avoid multiple detections of the same edge, an occupancy mask,
 initially empty, collects the dilated points of all the blurred segments,
-so that these points can not be add to another segment.
+so that these points can not be added to another segment.
 
 First the positions $M_j$ of the prominent local maxima of the gradient
 magnitude found under the stroke are sorted from the highest to the lowest.