How much handwritten text is needed for text-independent writer verification and identification

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

doi:10.1109/icpr.2008.4761908

Cited by 27 publications

(26 citation statements)

References 7 publications

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“…When using less powerful features, the text has to be longer than 200 characters. According to [2] the drawback of methods using textural features is that these features are not as discriminative as calculated on characters. Thus, more text is needed for the correct identification of a writer.…”

Section: Introductionmentioning

confidence: 99%

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Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies

Fiel

Sablatnig

2013

2013 12th International Conference on Document Analysis and Recognition

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In this paper a method for writer identification and writer retrieval is presented. Writer identification is the task of identifying the writer of a document out of a database of known writers. In contrast to identification, writer retrieval is the task of finding documents in a database according to the similarity of handwritings. The approach presented in this paper uses local features for this task. First a vocabulary is calculated by clustering features using a Gaussian Mixture Model and applying the Fisher kernel. For each document image the features are calculated and the Fisher Vector is generated using the vocabulary. The distance of this vector is then used as similarity measurement for the handwriting and can be used for writer identification and writer retrieval. The proposed method is evaluated on two datasets, namely the ICDAR 2011 Writer Identification Contest dataset which consists of 208 documents from 26 writers, and the CVL Database which contains 1539 documents from 309 writers. Experiments show that the proposed methods performs slightly better than previously presented writer identification approaches.

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Section: Introductionmentioning

confidence: 99%

“…Brink et al [2] show that using strong features 100 characters are sufficient for writer identification. When using less powerful features, the text has to be longer than 200 characters.…”

Section: Introductionmentioning

confidence: 99%

Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies

Fiel

Sablatnig

2013

2013 12th International Conference on Document Analysis and Recognition

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“…A key factor in building effective identification/verification systems relates to the amount of data required to build the underlying models [1]. Determining data sufficiency bounds therefore, provides a theoretical basis for systematically investigating and comparing different models.…”

Section: Introductionmentioning

confidence: 99%

Data Sufficiency for Online Writer Identification: A Comparative Study of Writer-Style Space vs. Feature Space Models

Shivram

Ramaiah

Govindaraju

2014

2014 22nd International Conference on Pattern Recognition

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A key factor in building effective writer identification/verification systems is the amount of data required to build the underlying models. In this research we systematically examine data sufficiency bounds for two broad approaches to online writer identification -feature space models vs. writer-style space models. We report results from 40 experiments conducted on two publicly available datasets and also test identification performance for the target models using two different feature functions. Our findings show that the writer-style space model gives higher identification performance for a given level of data and further, achieves high performance levels with lesser data costs. This model appears to require as less as 20 words per page to achieve identification performance close to 80% and reaches more than 90% accuracy with higher levels of data enrollment.

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“…For example, two writers may show differences in the word proportion but share common characteristics in word spacing, while the word proportion may be a distinctive pattern of a third writer. Instead of developing a universal model for identifying scribal hands as in previous works in automatic writer identification/verification [12,[18][19][20][21][22][23][24][25], we suggest that it is more useful to analyse the handwriting features individually. As previously discussed, scribal hand identification/verification is only one of the questions in palaeographic analysis, and the handwriting features can be analysed in relation to many different questions.…”

Section: Analysis Of the Handwriting Featuresmentioning

confidence: 99%

“…Also, for legal reasons, researchers are motivated to validate the individuality of handwriting through the implementation of objective quantitative measurements [15][16][17]. Automatic writer identification systems using pattern recognition methods have been implemented in a number of studies [18][19][20][21][22][23][24], with direct applications in historical manuscripts in [12,19,25]. The approaches established in the studies related to automatic writer identification are in fact similar to the solutions to digital palaeography in the global category.…”

Section: Automatic Writer Identificationmentioning

confidence: 99%

Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography

Liang

Fairhurst

Guest

et al. 2016

Int. J. Patt. Recogn. Artif. Intell.

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Digital palaeography is an emerging research area which aims to introduce digital image processing techniques into palaeographic analysis for the purpose of providing objective quantitative measurements. This paper explores the use of a fully automated handwriting feature extraction, visualisation and analysis system for digital palaeography which bridges the gap between traditional and digital palaeography in terms of the deployment of feature extraction techniques and handwriting metrics. We propose the application of a set of features, more closely related to conventional palaeographic assesment metrics than those commonly adopted in automatic writer identification. These features are emprically tested on two datasets in order to assess their effectiveness for automatic writer identification and aid attribution of individual handwriting characteristics in historical manuscripts. Finally, we introduce tools to support visualisation of the extracted features in a comparative way, showing how they can best be exploited in the implementation of a content-based image retrieval (CBIR) system for digital archiving.

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How much handwritten text is needed for text-independent writer verification and identification

Cited by 27 publications

References 7 publications

Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies

Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies

Data Sufficiency for Online Writer Identification: A Comparative Study of Writer-Style Space vs. Feature Space Models

Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography

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