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Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor
Saïdani, Asma (University of Tunis)
Kacem Echi, Afef (University of Tunis)
Belaïd, Abdel (University of Lorraine)

Data: 2015
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Script and type identification ; Histogram of oriented gradients ; Arabic and latin separation
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 núm. 2 (2015) , p. 1-23 (Regular Issue) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.762

23 p, 667.6 KB

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