A Database for Arabic Handwritten Text Image Recognition and Writer Identification

Mezghani, Anis; Kanoun, Slim; Khemakhem, Maher; Abed, Haikal El

doi:10.1109/icfhr.2012.155

Cited by 49 publications

(17 citation statements)

References 8 publications

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“…The Arabic Handwritten Text Images Database by Multiple Writers (AHTID/MW) [153] has been developed to support research in the Arabic handwriting segmentation and recognition. In addition, the database can also be employed to evaluate the writer identification systems.…”

Section: Ahtid/mwmentioning

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: Ahtid/mwmentioning

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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“…Notice that GMMs are well-known multipurpose and powerful modeling tools able to model thinly the distributions of features that are quite wide-scoped and largely multi-dimensional. Experiments used three public databases of Arabic and French: AHTID/MW [27], the APTI database [32] (composed of low-resolution (70 dpi) and synthetically generated images) and RIMES [21]. These experiments showed a remarkable performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

Saïdani

Kacem

Belaïd

2015

ELCVIA

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In this paper, we present an approach for Arabic and Latin script and its type identification based on Histogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writing orientation analysis. Then, they are extended to word image partitions to capture fine and discriminative details. Pyramid HOG are also used to study their effects on different observation levels of the image. Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs of pixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potential informative features combinations which maximizes the classification accuracy. The output is a relatively short descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set of words, extracted from standard databases, show that our identification system is robust and provides good word script and type identification: 99.07% of words are correctly classified.

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“…The Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) (Mezghani et al, 2012) consists of 3710 text lines and 22,896 words written by 53 native Arabic writers of different ages and educational backgrounds. The text samples are scanned in grayscale format at a resolution of 300 dpi.…”

Section: Ahtid/mw Databasementioning

confidence: 99%

“…The main components of our proposed system include local descriptor computation using Discrete Cosine Transform, multiple vector quantisation using bagging and clustering, structured writer representation via localised histograms of vector codes, dimensionality reduction using kernel discriminant analysis and classification using nearest centre rule. The proposed system has been evaluated on four hand written datasets including IAM (Marti and Bunke, 2002), CVL (Kleber et al, 2013), AHTID/MW (Mezghani et al, 2012) and IFN/ENIT (Pechwitz et al, 2002). The results achieved show that the system delivers comparable performance with state-of-the-art systems in the case where query documents are presented in ideal conditions on one hand.…”

Section: Introductionmentioning

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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A Database for Arabic Handwritten Text Image Recognition and Writer Identification

Cited by 49 publications

References 8 publications

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

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

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

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

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