Writer identification using texture descriptors of handwritten fragments

Hannad, Yaâcoub; Siddiqi, Imran; Kettani, Mohamed El Youssfi El

doi:10.1016/j.eswa.2015.11.002

Cited by 103 publications

(48 citation statements)

References 15 publications

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“…This outperforms by 0.2% the nearest best system developed by (Jain and Doermann, 2014). For the AHTID/MW database, 71.6% of Top 1 accuracy has been obtained with the proposed system which is still comparable to the state of the art, outperformed only by (Hannad et al, 2016). For the IFN/ENIT database, however, the system shows a clear drop in performance.…”

Section: Comparison Of Our Proposed System With Existing Workmentioning

confidence: 78%

“…These noisy versions of the database were used to record the Top 1 accuracy of the proposed system along with two other systems previously published in literature i.e. the systems proposed by and (Hannad et al, 2016). Furthermore, a variation of our proposed system was also applied on the noisy databases, where SIFT was used for feature extraction in place of DCT.…”

Section: Robustness Of the Proposed Systemmentioning

confidence: 99%

“…All the documents are scanned at 300 dpi and are saved in binary image format. The database is arranged similar to as explained in (Hannad et al, 2016) where a reduced set of samples are used per writer in order to simulate real world conditions. 30 randomly selected words per writer are used for training while 20 words per writer are used for testing.…”

Section: Ifn/enit Databasementioning

confidence: 99%

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Robust off-line text independent writer identification using bagged discrete cosine transform features

Khan

Tahir

Khelifi

et al. 2017

Expert Systems with Applications

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) AbstractEfficient writer identification systems identify the authorship of an unknown sample of text with high confidence. This has made automatic writer identification a very important topic of research for forensic document analysis. In this paper, we propose a robust system for offline text independent writer identification using bagged discrete cosine transform (BDCT) descriptors. Universal codebooks are first used to generate multiple predictor models. A final decision is then obtained by using the majority voting rule from these predictor models. The BDCT approach allows for DCT features to be effectively exploited for robust hand writer identification. The proposed system has first been assessed on the original version of hand written documents of various datasets and results have shown comparable performance with state-of-the-art systems. Next, blurry and noisy documents of two different datasets have been considered through intensive experiments where the system has been shown to perform significantly better than its competitors. To the best of our knowledge this is the first work that addresses the robustness aspect in automatic hand writer identification. This is particularly suitable in digital forensics as the documents acquired by the analyst may not be in ideal conditions.

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Section: Comparison Of Our Proposed System With Existing Workmentioning

confidence: 78%

Section: Robustness Of the Proposed Systemmentioning

confidence: 99%

Section: Ifn/enit Databasementioning

confidence: 99%

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Robust off-line text independent writer identification using bagged discrete cosine transform features

Khan

Tahir

Khelifi

et al. 2017

Expert Systems with Applications

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“…Abdi and Khemakhem (2015) reported an accuracy rate of 90% for 411 writers from IFN/ENIT. Hannad et al (2016) reported an accuracy rate of 94.89% for 411 writers data from IFN/ENIT database. The comparative writer identification rates are presented in …”

Section: Results Analysismentioning

confidence: 99%

“…Hannad et al (2016) introduced one more statistical feature Local Phase Quantization (LTP) in addition to LBP and LPQ. They reported an accuracy rate of 87% for 130 writers data taken from IFN/ENIT database.…”

Section: Feature Extractionmentioning

confidence: 99%

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

Saba

2018

Journal of Computer Science

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Arabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number of training epochs to "learn" from input data for generalization. The extracted features are fed to Fuzzy ARTMP for training and testing. Fuzzy ARTMAP is employed for the first time along with a novel fusion of statistical features for Arabic writer identification. The entire IFN/ENIT database is used in experiments such that 75% handwritten Arabic words from 411 writers are employed in training and 25% for testing the system at random. Several combinations of extracted features are tested using fuzzy ARTMAP classifier and finally one combination exhibited promising accuracy of 94.724% for Arabic writer identification on IFN/ENIT benchmark database.

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Handcrafted Descriptors-Based Effective Framework for Off-line Text-Independent Writer Identification

Chahi

Merabet

Ruichek

et al. 2019

Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)

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Writer identification using texture descriptors of handwritten fragments

Cited by 103 publications

References 15 publications

Robust off-line text independent writer identification using bagged discrete cosine transform features

Robust off-line text independent writer identification using bagged discrete cosine transform features

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

Handcrafted Descriptors-Based Effective Framework for Off-line Text-Independent Writer Identification

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