Writer identification of Chinese handwriting documents using hidden Markov tree model

He, Zhenyu; You, Xinge; Tang, Yuan Yan

doi:10.1016/j.patcog.2007.08.017

Cited by 105 publications

(37 citation statements)

References 32 publications

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“…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.

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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.

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Section: Scriptmentioning

confidence: 99%

Writer verification based on a single handwriting word samples

Paquet

2016

J Image Video Proc.

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“…A major contribution by Bangy Li et al [10], again in 2007, used the feature vector of hierarchical structure in shape primitives along with the dynamic and static feature for writer identification for 242 writers using NLPR online database and attained a result of above 90% [12]. Also, these authors have used a combination of general Gaussian model (GGD) and wavelet transform on Chinese handwriting in Ref.…”

Section: Chinese English and Other Languagesmentioning

confidence: 99%

A Survey on Writer Identification Schemes

Sreeraj¹,

Idicula²

2011

IJCA

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This paper presents a survey of the literature on writer identification schemes and techniques up till date. The paper outlines an overview of the writer identification schemes mainly in Chinese, English, Arabic and Persian languages. Taxonomy of different features adopted for online and offline writer identification schemes is also drawn at. The feature extraction methods adopted for the schemes are discussed in length outlining the merits and demerits of the same. In automated writer identification, text independent and text dependent methods are available which is also discussed in this paper. An evaluation of writer identification schemes under multiple languages is also analyzed by comparing the recognition rate.

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“…In recent systems, in order to avoid this problem, the input data has been normalized, so that the system may be more independent to the words of the text [1,2,7].…”

Section: Classification Criteriamentioning

confidence: 99%

A writer identification method based on XGabor and LCS

Helli

Moghaddam

2009

IEICE Electron. Express

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Writer identification is a popular research field in many languages such as English, Persian, Chinese, etc. The approaches of writer identification methods are dependent on the language because different languages letters have different pattern. In this paper, we have presented XGabor filter and proposed a language independent writer identification system. In the feature extraction phase of proposed method, Gabor and XGabor filters are used while in the classification phase, a new classification method is defined that is not based on any kind of distances among feature vectors. The proposed classifier uses the sequence similarity of the Sorted Order of Features (SOF). To measure this similarity, the Longest Common Subsequence (LCS) algorithm is employed. In simulation phase, two databases in different languages have been used. First one consisted of 100 people's Persian handwritings and second one had 30 people's English handwritings. The accuracy of the system was satisfactory in both of them.

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Writer identification of Chinese handwriting documents using hidden Markov tree model

Cited by 105 publications

References 32 publications

Writer verification based on a single handwriting word samples

Writer verification based on a single handwriting word samples

A Survey on Writer Identification Schemes

A writer identification method based on XGabor and LCS

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