Individuality of Bangla numerals

Abstract

This paper presents analysis of individuality of handwritten Bangla numerals. It has a great prospect in Writer Identification, Writer Verification, Forensic Science etc. After collecting and extracting characters from filled in forms, 400 dimensional feature vectors is computed based on gradient of the images. A total of 450 documents were used for this work. In our experiment we have used LIBLINEAR classifier of WEKA environment. We have computed and analyzed the Individuality of each numeral and observed that the numeral 5 has the most individuality property than other numerals and 0 has the least. We have also done the writer identification with all the numerals and obtained 96.5% accuracy with all writers.