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The Levenshtein algorithm (also called Edit-Distance) calculates the least number of edit operations that are necessary to modify one string to obtain another string. The most common way of calculating this is by the dynamic programming approach. A matrix is initialized measuring in the (m,n)-cell the Levenshtein distance between the m-character prefix of one with the n-prefix of the other word. The matrix can be filled from the upper left to the lower right corner. Each jump horizontally or vertically corresponds to an insert or a delete, respectively. The cost is normally set to 1 for each of the operations. The diagonal jump can cost either one, if the two characters in the row and column do not match or 0, if they do. Each cell always minimizes the cost locally. This way the number in the lower right corner is the Levenshtein distance between both words.

The activity accepts two string and returns a similarity percentage in the type System.Single  between two strings using Levenshtein Algorithm.

RPA Developer

Hi Ekankika,Thanks a million for your work - its very useful. I have a question regarding the output value. Usually, the max distance is 2 - acceptable for matching. However, I get different outputs when changing 2 characters in various example. Do you have a value for the acceptable value for the output of the activity? For example, 60 means 50% match etc or character difference of 2 etc.? Please let me know asap. Thanks, Arsha

In Levenshtein distance, zero means they are same. In general, this means smaller the value, the more similar. However output of the activity seems from the following expression.

output = 100 * (Max(L1, L2) - D) / Max(L1, L2)

L1: Length of String1, L2: Length of String2, D:Levenshtein distance

So 100 means they are same and smaller the value, the less similar .

My question is,  Is the above expression is right? If so, I think it's better to specify this in the manual.

 - Great work on this activity, Kumari!! 
This is a super helpful activity. Sometimes OCR isn't perfect, and there is a character or two that are different. A normal string comparison would fail, whereas this activity would succeed greatly and allow me to see how different the strings really are.
Looking forward to more valuable activities from you!


 - Well done!
Used it in string matching where OCR doesn't work perfectly.


Works very well. I used it to negate poor user input when searching through lists. Great job on this!

Levenshtein Distance Algorithm-String Comparison

Levenshtein Distance Algorithm-String Comparison

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