Skip to Main content Skip to Navigation
Journal articles

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

Asma Saïdani 1 Afef Kacem 1 Belaïd Abdel 2 
2 READ - Recognition of writing and analysis of documents
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : 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.
Document type :
Journal articles
Complete list of metadata
Contributor : Abdel Belaid Connect in order to contact the contributor
Submitted on : Tuesday, January 12, 2016 - 1:47:19 PM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM

Links full text




Asma Saïdani, Afef Kacem, Belaïd Abdel. Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor. Electronic Letters on Computer Vision and Image Analysis, Computer Vision Center Press, 2015, 2 (14), pp.24. ⟨10.5565/rev/elcvia.762⟩. ⟨hal-01254539⟩



Record views