Binarization of Color Characters in Scene Images Using k-means Clustering and Support Vector Machines
Kohei Kita, Toru Wakahara · 2010
This paper proposes a new technique for binalizing multicolored characters subject to heavy degradations. The key ideas are threefold. The first is generation of tentatively binarized images via every dichotomization of k clusters obtained by k-means clustering in the HSI color space. The total number of tentatively binarized images equals 2k-2. The second is use of support vector machines (SVM) to determine whether and to what degree each tentatively binarized image represents a character or non-character. We feed the SVM with mesh and weighted direction code histogram features to output the degree of “character-likeness.” The third is selection of a single binarized image with the maximum degree of “character likeness” as an optimal binarization result. Experiments using a total of 1000 single-character color images extracted from the ICDAR 2003 robust OCR dataset show that the proposed method achieves a correct binarization rate of 93.7%.