Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

In this paper, we present an approach for Arabic and Latin script and its type identification based on
Histogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writing
orientation analysis. Then, they are extended to word image partitions to capture fine and discriminative
details. Pyramid HOG are also used to study their effects on different observation levels of the image.
Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs of
pixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potential
informative features combinations which maximizes the classification accuracy. The output is a relatively
short descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set of
words, extracted from standard databases, show that our identification system is robust and provides good
word script and type identification: 99.07% of words are correctly classified.