diff --git a/guide/english/algorithms/sorting-algorithms/merge-sort/index.md b/guide/english/algorithms/sorting-algorithms/merge-sort/index.md
index 48641fa308..a3972dcb78 100644
--- a/guide/english/algorithms/sorting-algorithms/merge-sort/index.md
+++ b/guide/english/algorithms/sorting-algorithms/merge-sort/index.md
@@ -25,18 +25,18 @@ T(n) = 2T(n/2) + n
Counting the number of repetitions of n in the sum at the end, we see that there are lg n + 1 of them. Thus the running time is n(lg n + 1) = n lg n + n. We observe that n lg n + n < n lg n + n lg n = 2n lg n for n>0, so the running time is O(n lg n). Another simple way to remember the time complexity O(n lg n) of merge sort is that it takes roughly log2n steps to split an array of size n to multiple arrays of size one. After each split, the algorithm have to merge 2 sorted arrays into one which can take n steps in total. As a result, the time complexity for merge sort is O(n lg n).
-```
-MergeSort(arr[], left, right):
-If right > l:
- 1. Find the middle point to divide the array into two halves:
- mid = (left+right)/2
- 2. Call mergeSort for first half:
- Call mergeSort(arr, left, mid)
- 3. Call mergeSort for second half:
- Call mergeSort(arr, mid+1, right)
- 4. Merge the two halves sorted in step 2 and 3:
- Call merge(arr, left, mid, right)
- ```
+
+MergeSort(arr[], left, right):
+If right > l:
+ 1. Find the middle point to divide the array into two halves:
+ mid = (left+right)/2
+ 2. Call mergeSort for first half:
+ Call mergeSort(arr, left, mid)
+ 3. Call mergeSort for second half:
+ Call mergeSort(arr, mid+1, right)
+ 4. Merge the two halves sorted in step 2 and 3:
+ Call merge(arr, left, mid, right)
+
