Resumen de Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor
Asma Saidani, Afef Kacem, Abdel Belaid
In this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. 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 ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified.
