Text-Independent Writer Identification and Verification on Offline Arabic Handwriting

Bulacu, M.; Schomaker, Lambert; Brink, Axel

doi:10.1109/icdar.2007.4377019

Cited by 85 publications

(76 citation statements)

References 6 publications

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“…In the generation stage, a new codebook will be produced known the Ending codebook as depicted in Figure 9. According to [9,22], different distance measure can be utilized for the comparison of two distributions of sample In the equation (Equation 3), and denote the two similarities (histograms) for comparison, denotes the segment i of the histogram and dim denotes the total number of histogram segments. The distance is calculated for the Ending codebook.…”

Section: Ementioning

confidence: 99%

Writer Identification on Multi-Script Handwritten Using Optimum Features

Ahmed

Hasan

Hameed

et al. 2017

KJAR

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Abstract:Recognizing the writer of a text that has been handwritten is a very intriguing research problem in the field of document analysis and recognition. This study tables an automatic way of recognizing the writer from handwritten samples. Even though much has been done in previous researches that have presented other various methods, it is still clear that the field has a room for improvement. This particular method uses Optimum Features based writer characterization. Here, each of the samples written is grouped according to their set of features that are acquired from a computed codebook. This proposed codebook is different from the others which segment the samples into graphemes by fragmenting a certain part of the writing known as ending strokes. The proposed technique is employed to a locate and extract the handwriting fragments from ending zone and then grouped the similar fragments to generate a new cluster known as ending cluster. The cluster that comes in handy in the process of coming up with the ending codebook through picking out the center of the same fragment group. The process is finalized by evaluating the proposed method on four datasets of the various languages. This method being proposed had an impressive 97.12% identification rate which is rates the best result on the ICFHR dataset.

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Section: Ementioning

confidence: 99%

Writer Identification on Multi-Script Handwritten Using Optimum Features

Ahmed

Hasan

Hameed

et al. 2017

KJAR

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“…All pages were scanned at 300 dpi as gray-scale images. The database has been mainly employed for evaluation of writer identification and verification systems [37,117].…”

Section: Firemaker Databasementioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

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“…Thus it is obvious, that this method can be extended to other languages by applying some changes on feature extraction phase. The difference between the two writer identifications schemes in [39] and [40] is that the former was used in English handwriting and got about 80% accuracy in top-1 results and about 92% in top-10 results while the latter supported Arabic handwriting and its accuracy was 88% in top-1 and 99% in top-10 results.…”

Section: Chinese English and Other Languagesmentioning

confidence: 99%

“…Bulacu et al proposed text-independent Arabic writer identification by combining some textural and allographic features [40,45]. After extracting textural features (mostly relations between different angles in each written pixel) a probability distribution function was generated and the nearest neighborhood classifier using the as a distance measure was used.…”

Section: Arabicmentioning

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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Text-Independent Writer Identification and Verification on Offline Arabic Handwriting

Cited by 85 publications

References 6 publications

Writer Identification on Multi-Script Handwritten Using Optimum Features

Writer Identification on Multi-Script Handwritten Using Optimum Features

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

A Survey on Writer Identification Schemes

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