Texture-based descriptors for writer identification and verification

Bertolini, Diego; Oliveira, Luiz S.; Justino, Edson J. R.; Sabourin, Robert

doi:10.1016/j.eswa.2012.10.016

Cited by 155 publications

(58 citation statements)

References 26 publications

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“…Regarding Arabic handwriting recognition, a high word recognition rate of 93.37 % [217] is reported on the IFN/ENIT database. Writer identification systems have been most evaluated and compared on the IAM database and a highest identification rate of 96.7 % is reported in [218] with one sample of each of the 657 writers in training and one in the test set. Like Arabic handwriting recognition, the writer identification systems targeting Arabic handwritings mostly employ the IFN/ENIT database.…”

Section: Experimental Protocols Evaluation Metrics and State-of-the-mentioning

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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Section: Experimental Protocols Evaluation Metrics and State-of-the-mentioning

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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“…The arrangement of these databases have been explained in Section 4. Using our proposed BDCT approach, a Top 1 accuracy of 97.2% on the IAM database has been achieved, which outperforms the nearest best performing system of (Bertolini et al, 2013) by 0.5%. For the CVL database, 99.6% of Top 1 accuracy has been reached by our system.…”

Section: Comparison Of Our Proposed System With Existing Workmentioning

confidence: 92%

“…It was demonstrated that using multiple codebooks to represent every writer produced better results than by using a single codebook approach. (Bertolini et al, 2013) considered the handwriting text as a texture and used Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) to extract textural features for writer verification and identification. They built upon a previously reported work using the dissimilarity framework approach by (Hanusiak et al, 2012) and extended the idea to writer identification.…”

Section: Related Workmentioning

confidence: 99%

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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“…In this line of action, Bertolini et al (2013) exhibited high writer identification rates by using a fusion of two statistical features; Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). Hannad et al (2016) introduced one more statistical feature Local Phase Quantization (LTP) in addition to LBP and LPQ.…”

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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Texture-based descriptors for writer identification and verification

Cited by 155 publications

References 26 publications

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

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

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

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

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