Co-occurrence Matrix of Oriented Gradients for Word Script and Nature Identification
Abstract
In this paper, we propose a new scheme for script
and nature identification. The objective is to discriminate between
machine-printed/handwritten and Latin/Arabic scripts at word
level. It is relatively a complex task due to possible use of multi-fonts and sizes, complexity and variation in handwriting. In the
proposed script identification system, we extract features from
word images using Co-occurrence Matrix of Oriented Gradients
(Co-MOG). The classification is done using k Nearest Neighbors
(k-NN) classifier. Extensive experimentation has been carried on
24000 words extracted from standard databases. An average
identification accuracy of 99.85% is achieved which clearly
outperforms results of some existing systems