An Efficient Post Processing Algorithm for Online Handwriting Gurmukhi Character Recognition using Set Theory

In this paper, a post processor for accuracy of character recognition of real-time online Gurmukhi script has been developed. Our analysis is based on dataset consisting of 184 samples of each 45 characters of Gurmukhi script collected from four different categories of writers. Based on this extensive study, we propose an efficient algorithm for online handwritten Gurmukhi character recognition that achieves promising recognition accuracy of 95.6% for single character stroke sequencing. Beside character recognition the contribution in this paper is summarized in two folds as (i) the proposed scheme resolves stroke sequencing, (ii) overwritten strokes are identified and resolved. Moreover, for every stroke, complexity of adding new stroke for Gurmukhi character formation has been computed to be O(n).

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