diff --git a/src/algorithms/string/levenshtein-distance/README.md b/src/algorithms/string/levenshtein-distance/README.md
index 00019e87..cc5b5e9d 100644
--- a/src/algorithms/string/levenshtein-distance/README.md
+++ b/src/algorithms/string/levenshtein-distance/README.md
@@ -40,7 +40,76 @@ three edits:
 2. sitt**e**n → sitt**i**n (substitution of "i" for "e")
 3. sittin → sittin**g** (insertion of "g" at the end).
 
+## Applications
+
+This has a wide range of applications, for instance, spell checkers, correction 
+systems for optical character recognition, fuzzy string searching, and software 
+to assist natural language translation based on translation memory.
+
+## Dynamic Programming Approach Explanation
+
+Let’s take a simple example of finding minimum edit distance between 
+strings `ME` and `MY`. Intuitively you already know that minimum edit distance 
+here is `1` operation and this operation. And it is a replacing `E` with `Y`. But 
+let’s try to formalize it in a form of the algorithm in order to be able to 
+do more complex examples like transforming `Saturday` into `Sunday`.
+
+To apply the mathematical formula mentioned above to `ME → MY` transformation 
+we need to know minimum edit distances of `ME → M`, `M → MY` and `M → M` transformations
+in prior. Then we will need to pick the minimum one and add _one_ operation to 
+transform last letters `E → Y`. So minimum edit distance of `ME → MY` transformation 
+is being calculated based on three previously possible transformations.
+
+To explain this further let’s draw the following matrix:
+
+![Levenshtein Matrix](https://cdn-images-1.medium.com/max/1600/1*2d46ug_PL5LfeqztckoYGw.jpeg)
+
+- Cell `(0:1)` contains red number 1. It means that we need 1 operation to 
+transform `M` to an empty string. And it is by deleting `M`. This is why this number is red.
+- Cell `(0:2)` contains red number 2. It means that we need 2 operations 
+to transform `ME` to an empty string. And it is by deleting `E` and `M`.
+- Cell `(1:0)` contains green number 1. It means that we need 1 operation 
+to transform an empty string to `M`. And it is by inserting `M`. This is why this number is green.
+- Cell `(2:0)` contains green number 2. It means that we need 2 operations 
+to transform an empty string to `MY`. And it is by inserting `Y` and  `M`.
+- Cell `(1:1)` contains number 0. It means that it costs nothing 
+to transform `M` into `M`.
+- Cell `(1:2)` contains red number 1. It means that we need 1 operation 
+to transform `ME` to `M`. And it is be deleting `E`.
+- And so on...
+
+This looks easy for such small matrix as ours (it is only `3x3`). But here you
+may find basic concepts that may be applied to calculate all those numbers for
+bigger matrices (let’s say `9x7` one, for `Saturday → Sunday` transformation).
+
+According to the formula you only need three adjacent cells `(i-1:j)`, `(i-1:j-1)`, and `(i:j-1)` to
+calculate the number for current cell `(i:j)`. All we need to do is to find the 
+minimum of those three cells and then add `1` in case if we have different 
+letters in `i`'s row and `j`'s column.
+
+You may clearly see the recursive nature of the problem.
+
+![Levenshtein Matrix](https://cdn-images-1.medium.com/max/2000/1*JdHQ5TeKiDlE-iKK1s_2vw.jpeg)
+
+Let's draw a decision graph for this problem.
+
+![Minimum Edit Distance Decision Graph](https://cdn-images-1.medium.com/max/1600/1*SGwYUpXH9H1xUeTvJk0e7Q.jpeg)
+
+You may see a number of overlapping sub-problems on the picture that are marked 
+with red. Also there is no way to reduce the number of operations and make it 
+less then a minimum of those three adjacent cells from the formula. 
+
+Also you may notice that each cell number in the matrix is being calculated 
+based on previous ones. Thus the tabulation technique (filling the cache in 
+bottom-up direction) is being applied here.
+
+Applying this principles further we may solve more complicated cases like 
+with `Saturday → Sunday` transformation.
+
+![Levenshtein distance](https://cdn-images-1.medium.com/max/1600/1*fPEHiImYLKxSTUhrGbYq3g.jpeg)
+
 ## References
 
 - [Wikipedia](https://en.wikipedia.org/wiki/Levenshtein_distance)
 - [YouTube](https://www.youtube.com/watch?v=We3YDTzNXEk&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [ITNext](https://itnext.io/dynamic-programming-vs-divide-and-conquer-2fea680becbe)