Artistic Multi-character Script Identification Using Iterative Isotropic Dilation Algorithm

In this work, a new problem of script identification named artistic multi-character script identification has been addressed. Two types of datasets of artistic documents/images prepared with Bangla, Devanagari and Roman script have been used: one is real life artistic multi-character script image and another is synthetic artistic multi-character script image. After binarization using Otsu’s algorithm, some character images found to be broken into components. To overcome this, a novel iterative isotropic dilation algorithm is proposed here to convert the components into a single component object. Then two types of features, namely shape based and texture based features have been considered. Discrete Gabor wavelet has been exploited with 2 scales and 4 orientations for texture feature extraction and PCA is used to reduce the dimensionality of the texture feature space. The performance of the proposed algorithm has been tested with different machine learning classifiers and promising accuracy has been observed.

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