A text-independent Persian writer identification based on feature relation graph (FRG)

Helli, Behzad; Moghaddam, Mohsen Ebrahimi

doi:10.1016/j.patcog.2009.11.026

Cited by 62 publications

(39 citation statements)

References 29 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [12,18,21] used one to several pages; authors in [11] used paragraphs. However, only few studies [20,22] have tried to follow an experimental setup where only small samples are available to identify or authenticate the writer, facing similar conditions than forensic examiners may be confronted to.…”

Section: Samples: Nature and Sizementioning

confidence: 99%

See 1 more Smart Citation

Writer verification based on a single handwriting word samples

Paquet

2016

J Image Video Proc.

View full text Add to dashboard Cite

The writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or writer verification. However, all the researches assumed that they dispose of a large amount of text to identify or authenticate the writer, which is never the case in real-life applications. In this paper, we present an original approach for the writer authentication task based on the analysis of a unique sample of a handwriting word. We used the Levenshtein edit distance based on Fisher-Wagner algorithm to estimate the cost of transforming one handwritten word into another. Such method has been successfully applied for signature authentication and voice recognition. In order to apply it to handwriting words, we developed a segmentation module to generate the graphemes; considered as elementary components for each word. We evaluated this approach on part of the IAM database (100 writers), where half of them provided three samples only of the same word. The obtained results are very promising since we succeed to accept correctly in 87 % of cases when we used the whole database (100 writers) and up to 92 % when we used 40 writers.

show abstract

Section: Samples: Nature and Sizementioning

confidence: 99%

“…The authors in [21] developed a Persian writer identification system, [15] and [11] proposed a system for Arabic writers, [23] for Chinese writers, [19] for Japanese writers, and [24] for Telugu writers.…”

Section: Scriptmentioning

confidence: 99%

Writer verification based on a single handwriting word samples

Paquet

2016

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…He et al (2007He et al ( , 2008 proposed wavelet based generalized Gaussian model (GGD) and hidden Markov tree (HMT) model in wavelet domain to replace the traditional 2-D Gabor filter. The approach which texture features extracted by Gabor and XGabor filters are combined with feature relation graph (FRG) and showed high efficiency for Persian writer identification (Helli & Moghaddam, 2010). Edge based directional probability distributions and connected component contours as features for the writer identification task are proposed (Schomaker & Bulacu, 2004).…”

Section: Related Workmentioning

confidence: 99%

“…The combined Gabor filter and Independent Component Analysis (ICA) method indicated high accuracy in texture segmentation and classification (Chen, Y & Wang, R, 2006, 2007. So these reports about writer identifications are mostly based on Latin handwriting (Plamondon et al, 1989;Said et al, 2000;Schomaker & Bulacu, 2004;Srihari et al, 2002), and Chinese handwriting (He et al 2007(He et al , 2008Li et al, 2009), Arabic handwritings (Al-Dmour & Zitar, 2007, even Persian handwritings (Helli & Moghaddam, 2010). However, there are only 4 reports about Uyghur handwriting based writer identification, in which two of them are our previous research (Ubul et al, 2008(Ubul et al, , 2009 indicated to using Gabor filter and Genetic algorithm (GA), and Gabor filter plus PCA and ICA methods for feature extraction, and get the 92.5% identification rate for 55 different people.…”

Section: Related Workmentioning

confidence: 99%

Multi-Stage Based Feature Extraction Methods for Uyghur Handwriting Based Writer Identification

Ubul¹,

Adler²,

Mamatjan³

2012

Genetic Algorithms in Applications

View full text Add to dashboard Cite

“…Pattern recognition in handwriting considered as a wide-ranging term which covers all kinds of application field together with identification based on handwriting (Guo, Christian, & Alex, 2010), verification based on handwriting (Srihari, & Ball, 2009), authentication (Muzaffar, & Jurgen, 2009;Behzad, & Mohsen, 2010) and character recognition (Tonghua, Zhang, Guan, & Huang, 2009;Bayan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Invarianceness for Character Recognition Using Geo-Discretization Features

Ali¹,

Omer²

2016

CIS

View full text Add to dashboard Cite

Recognition rate of characters in the handwritten is still a big challenge for the research because of a shape variation, scale and format in a given handwritten character. A more complicated handwritten character recognition system needs a better feature extraction technique that deal with such variation of hand writing. In other hand, to obtain efficient and accurate recognition rely on off-line English handwriting character, the similarity in the character traits is an important issue to be differentiated in an off-line English handwriting to. In recognizing a character, character handwriting format could be implicitly analyzed to make the representation of the unique hidden features of the individual's character is allowable. Unique features can be used in recognizing characters which can be considerable when the similarity between two characters is high. However, the problem of the similarity in off-line English character handwritten was not taken into account thus, leaving a high possibility of degrading the similarity error for intra-class [same character] with the decrease of the similarity error for inter-class [different character]. Therefore, in order to achieve better performance, this paper proposes a discretization feature algorithm to reduce the similarity error for intra-class [same character]. The mean absolute error is used as a parameter to calculate the similarity between inter and/or intra class characters. Test results show that the identification rate give a better result with the proposed hybrid Geo-Discretization method.

show abstract

A text-independent Persian writer identification based on feature relation graph (FRG)

Cited by 62 publications

References 29 publications

Writer verification based on a single handwriting word samples

Writer verification based on a single handwriting word samples

Multi-Stage Based Feature Extraction Methods for Uyghur Handwriting Based Writer Identification

Invarianceness for Character Recognition Using Geo-Discretization Features

Contact Info

Product

Resources

About