Pyramid histogram of oriented gradient for machine-printed/handwritten and Arabic/Latin word discrimination

Saïdani, Asma; Echi, Afef Kacem

doi:10.1109/socpar.2014.7008017

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…We formalized the component methodology by creating a meta-model of all software aspects of components to allow automated processing of the functional and non-functional properties of components and services. We discussed how contextawareness can be used for composition of componentbased services using our previous work on context modelling [16], specification of pervasive services [14] and context management support [15]. We illustrated the core ideas of our infrastructure support by means of an example, and gave an outline of all the steps in the procedure to achieve a customized service.…”

Section: Discussionmentioning

confidence: 99%

“…which are not script dependent) are not proficient to work with small size images like word images. Recently, we respectively investigate the use of black run lengths based features and pyramid histogram of oriented gradients, to exploit the writing orientation and we achieved satisfactory results, in [15] [14]. But, we noted that including co-occurrence with various positional offsets, the features descriptors can express complex shapes of writing with local and global distributions of gradient orientations.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Script and Type Identification in Bi-lingual Forms

Echi¹

2016

IJCIS

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Script and Type Identification in Bi-lingual Forms

Echi¹

2016

IJCIS

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“…Input strings were represented by pyramidal histograms of characters (PHOC) [36] to be encoded at different levels. Also, pyramid histogram of oriented gradients (PHoG) [37] outperformed the ordinary histograms of gradients (HoG) [7], [20], [38]. Based on literature [5], [39], derivatives were also computed in recently proposed systems.…”

Section: Features Extractionmentioning

confidence: 99%

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multistage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially interrelated clusters. The interrelations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems. INDEX TERMS Agglomerative hierarchical clustering, deep convolutional neural network, Kullback-Leibler divergence, offline arabic handwritting recognition, pyramid histogram of gradients.

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“…we cite now the most recent work that addresses the same issue, which are three works: [15], [30] and [28]. In [15] present a performance comparison of curvelet, dual-tree complex wavelet and discrete wavelet transform in handwritten words classification (Arabic and Latin).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In our former work [30], we used pyramid histogram of oriented gradients (PHOG) to discriminate Arabic/Latin and handwritten/printed words. PHOG features, counts occurrences of gradient orientation in localized portion of an image.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Saïdani

Kacem

Belaïd

2015

ELCVIA

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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.

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Pyramid histogram of oriented gradient for machine-printed/handwritten and Arabic/Latin word discrimination

Cited by 14 publications

References 12 publications

Automatic Script and Type Identification in Bi-lingual Forms

Automatic Script and Type Identification in Bi-lingual Forms

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

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

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