OCR: weighted Levenshtein distance

Question

I\'m trying to create an optical character recognition system with the dictionary.

In fact I don\'t have an implemented dictionary yet=)

I\'ve heard that there a

Answer 1

Here is an example (C#) where weight of "replace character" operation depends on distance between character codes:

      static double WeightedLevenshtein(string b1, string b2) {
        b1 = b1.ToUpper();
        b2 = b2.ToUpper();

        double[,] matrix = new double[b1.Length + 1, b2.Length + 1];

        for (int i = 1; i <= b1.Length; i++) {
            matrix[i, 0] = i;
        }

        for (int i = 1; i <= b2.Length; i++) {
            matrix[0, i] = i;
        }

        for (int i = 1; i <= b1.Length; i++) {
            for (int j = 1; j <= b2.Length; j++) {
                double distance_replace = matrix[(i - 1), (j - 1)];
                if (b1[i - 1] != b2[j - 1]) {
                    // Cost of replace
                    distance_replace += Math.Abs((float)(b1[i - 1]) - b2[j - 1]) / ('Z'-'A');
                }

                // Cost of remove = 1 
                double distance_remove = matrix[(i - 1), j] + 1;
                // Cost of add = 1
                double distance_add = matrix[i, (j - 1)] + 1;

                matrix[i, j] = Math.Min(distance_replace, 
                                    Math.Min(distance_add, distance_remove));
            }
        }

        return matrix[b1.Length, b2.Length] ;
    }

You see how it works here: http://ideone.com/RblFK

Answer 2

A few years too late but the following python package (with which I am NOT affiliated) allows for arbitrary weighting of all the Levenshtein edit operations and ASCII character mappings etc.

https://github.com/infoscout/weighted-levenshtein

pip install weighted-levenshtein

Also this one (also not affiliated):

https://github.com/luozhouyang/python-string-similarity

Answer 3

This might be what you are looking for: http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance (and kindly some working code is included in the link)

Update:

http://nlp.stanford.edu/IR-book/html/htmledition/edit-distance-1.html