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In any multi-script environment, handwritten script classification is ofparamount importance before the document images are fed to their respectiveOptical Character Recognition (OCR) engines. Over the years, this complexpattern classification problem has been solved by researchers proposing variousfeature vectors mostly having large dimension, thereby increasing thecomputation complexity of the whole classification model. Feature Selection(FS) can serve as an intermediate step to reduce the size of the featurevectors by restricting them only to the essential and relevant features. In ourpaper, we have addressed this issue by introducing a new FS algorithm, calledHybrid Swarm and Gravitation based FS (HSGFS). This algorithm is made to run on3 feature vectors introduced in the literature recently - Distance-HoughTransform (DHT), Histogram of Oriented Gradients (HOG) and Modified log-Gabor(MLG) filter Transform. Three state-of-the-art classifiers namely, Multi-LayerPerceptron (MLP), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM)are used for the handwritten script classification. Handwritten datasets,prepared at block, text-line and word level, consisting of officiallyrecognized 12 Indic scripts are used for the evaluation of our method. Anaverage improvement in the range of 2-5 % is achieved in the classificationaccuracies by utilizing only about 75-80 % of the original feature vectors onall three datasets. The proposed methodology also shows better performance whencompared to some popularly used FS models.

A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem