Printed Numeral Recognition Using Structural and Skeleton Features
DOI:
https://doi.org/10.26438/ijcse/v6i11.224232Keywords:
Structural, Skeleton Features.K-nn, Classification, Watersheds, contoursAbstract
In automatic numeral digit recognition system, feature collection is most important aspect for achieving high recognition performance. To attain this, we proposes model for printed numeral digit recognition using number of contours, skeleton features such as number of end points, number of horizental and vertical crossings Number of watersheds, and ratio between the number of foreground pixels in upper half-part and lower half-part of the numerical digit image. Based on these features the present study designed user defined classification algorithm for printed numerical digit recognition. To find the effectiveness of the proposed algorithm, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier and other classification algorithms to evaluate the results. The experimental results prove that the proposed features are well suited for printed digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size and shape invariant.
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