In this paper a two step method for the recognition of printed subwords is proposed. Using characteristic loci features, the set of printed subwords are clustered into 300 clusters by k-means algorithm. Each cluster is represented by its mean. In the first step, each in
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In this paper a two step method for the recognition of printed subwords is proposed. Using characteristic loci features, the set of printed subwords are clustered into 300 clusters by k-means algorithm. Each cluster is represented by its mean. In the first step, each input is classified into 300 categories by minimum Euclidian distance from the cluster centers, and 10 closest clusters are found. In the second step, Fourier descriptors of the subword contour are used to classify the input subword into the members of these 10 clusters.
The training set consists of 12700 Farsi subwords in 4 different fonts, Lotus, Mitra, Yagut and Zar, and 3 sizes of 10, 12 and 14. In a test, a set of 500 subwords was used. Considering the first class, top five and top ten classes, 71.4%, 95%, and 98.2% of these subwords were correctly classified. In the post processing, dots of the subword and their positions were used to improve the recognition results. This improved the recognition rate to 92.6%.
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