A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

Slimane, Fouad; Kanoun, Slim; Hennebert, Jean; Alimi, Adel M.; Ingold, Rolf

doi:10.1016/j.patrec.2012.09.012

Cited by 63 publications

(34 citation statements)

References 39 publications

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“…Ding et al [9] used modified quadratic discriminant functions to enhance classification power. Slimane et al [11] used Gaussian mixture models to build an Arabic font recognition system. Zhang et al [12] used a support vector machine to distinguish among 25 types of Chinese fonts.…”

Section: Related Studiesmentioning

confidence: 99%

“…In the experi -ments, each single image was divided into [3] 95. 45 94.48 SDIP [5] 93.00 91.60 MFA [34] 91.60 90.10 regions, and a maximum of 13 regions were randomly dropped.…”

Section: Evaluation Of Dropregion In Comparison To Dropoutmentioning

confidence: 99%

“…Font recognition is an inherently difficult and error-prone task because of the huge number of available fonts, the dynamic and open-ended properties of font classes, and the very subtle and character-dependent differences among fonts [1]. Over the years, some approaches to font recognition have been proposed [2][3][4][5][6][7][8], but most font recognition research has been carried out on text in Western languages, and the approaches proposed have not yielded satisfactory accuracy. Because of the structure and ideographic nature of individual Chinese characters, Chinese font recognition is more challenging than recognition of most Western scripts.…”

Section: Introductionmentioning

confidence: 99%

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DropRegion training of inception font network for high-performance Chinese font recognition

Huang

Zhong

Jin

et al. 2018

Pattern Recognition

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Chinese font recognition (CFR) has gained significant attention in recent years. However, due to the sparsity of labeled font samples and the structural complexity of Chinese characters, CFR is still a challenging task. In this paper, a DropRegion method is proposed to generate a large number of stochastic variant font samples whose local regions are selectively disrupted and an inception font network (IFN) with two additional convolutional neural network (CNN) structure elements, i.e., a cascaded cross-channel parametric pooling (CCCP) and global average pooling, is designed. Because the distribution of strokes in a font image is non-stationary, an elastic meshing technique that adaptively constructs a set of local regions with equalized information is developed. Thus, DropRegion is seamlessly embedded in the IFN, which enables end-to-end training; the proposed DropRegion -IFN can be used for high performance CFR. Experimental results have confirmed the effectiveness of our new approach for CFR.

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Section: Related Studiesmentioning

confidence: 99%

“…In the experi -ments, each single image was divided into [3] 95. 45 94.48 SDIP [5] 93.00 91.60 MFA [34] 91.60 90.10 regions, and a maximum of 13 regions were randomly dropped.…”

Section: Evaluation Of Dropregion In Comparison To Dropoutmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DropRegion training of inception font network for high-performance Chinese font recognition

Huang

Zhong

Jin

et al. 2018

Pattern Recognition

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“…Unilingual: When a particular document under identification consists of only one script the identification is called as the unilingual script identification [28]. ii.…”

Section: E Number Of Scriptsmentioning

confidence: 99%

Density Based Script Identification of a Multilingual Document Image

Bashir¹,

Quadri²

2015

IJIGSP

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“…Gaussian Mixture Models (GMMs) were used in many domains such as font recognition [8], [9], label image verification [10], baseline estimation [11], writer identification [12], etc. In this work GMMs are used to model the handwriting style of each historical book(s).…”

Section: B Gaussian Mixture Models For Handwriting Style Identificatmentioning

confidence: 99%

GMM-based handwriting style identification system for historical documents

Slimane

Schaban

Märgner

2014

2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)

Self Cite

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Abstract-In this paper, we describe a novel method for handwriting style identification. A handwriting style can be common to one or several writer. It can represent also a handwriting style used in a period of the history or for specific document. Our method is based on Gaussian Mixture Models (GMMs) using different kind of features computed using a combined fixed-length horizontal and vertical sliding window moving over a document page. For each writing style a GMM is built and trained using page images. At the recognition phase, the system returns log-likelihood scores. The GMM model with the highest score is selected. Experiments using page images from historical German document collection demonstrate good performance results. The identification rate of the GMM-based system developed with six historical handwriting style is 100%.

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A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

Cited by 63 publications

References 39 publications

DropRegion training of inception font network for high-performance Chinese font recognition

DropRegion training of inception font network for high-performance Chinese font recognition

Density Based Script Identification of a Multilingual Document Image

GMM-based handwriting style identification system for historical documents

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