Artistic multi-script identification at character level with extreme learning machine

In this work, a novel problem, namely artistic multi-script identification at character level has been addressed. Two types of documents: real/ natural and synthetic have been used for dataset preparation. After binarizing using Otsu’s global thresholding algorithm, a semi-automatic segmentation technique has been applied for character separation. Some well-known texture based features have been considered from the segmented images and further, they have been converted into lower dimensional space by applying principal component analysis. Those final feature set are classified using an Extreme Learning based classifier and performance are compared with traditional machine learning techniques and other features. Observing the inherent complexity of the multi-script character level datasets, an encouraging outcome has been obtained.