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 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

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 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

FACE RECOGNITION USING CO-OCCURRENCE HISTOGRAMS OF ORIENTED GRADIENTS

International audienceRecently, Histograms of Oriented Gradient (HOG) are applied in face recognition. Their extension, Histograms of cooccurrence of Oriented Gradient (CoHOG), which enhance spatial information were applied in pedestrian detection problem. In this paper, CoHOG are applied on the face recognition problem. Some weighted functions for magnitude gradient are tested. We also propose a weighted approach for CoHOG, where a weight value is set for each subregion of a face image. Numerical experiments performed on Yale and ORL datasets show that (i) CoHOG has a recognition accuracy higher than HOG; (ii) using gradient magnitude in CoHOG improves recognition results; and (iii) weighted CoHOG approach improves the accuracy recognition rate. The recognition results using CoHOG are competitive with some of the state of the art methods, proving the effectiveness of CoHOG descriptor for face recognition