A computer-based system to support forensic studies on handwritten documents

Franke, Katrin; Köppen, Mario

doi:10.1007/pl00013565

Cited by 66 publications

(24 citation statements)

References 21 publications

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“…To facilitate both human perception and machine recognition, these distortions must be corrected. Early work to correct bleed-through distortion on historical document images focused primarily on threholding techniques such as multistage thresholding [8], local adaptive filters [4] and noise-based thresholding [2]. These methods however are limited as they assume distinct intensities for foreground text, bleed-through and background in a single image.…”

Section: Introductionmentioning

confidence: 99%

Automatic Corresponding Control Points Selection for Historical Document Image Registration

Wang

Brown

Tan

2009

2009 10th International Conference on Document Analysis and Recognition

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Image registration is crucial for various image analysis tasks. In particular, most approaches to correction of bleed-through distortion on handwritten document images require the recto image and the verso image to be precisely registered. In this paper, we present a fully automatic method which detects specific number of corresponding control points from historical documents for the purpose of registration. First, candidate points are located by inspecting the gradient direction maps of document images. Corresponding control points are selected based on a dissimilarity metric that incorporates image intensity, gradient magnitude, gradient orientation and displacement. To improve the quality of the detected control points, median filers and consistency checking are applied to correct mismatches. Experiments on real historical document images have shown encouraging results and further improvements can be made by exploiting more sophisticated similarity metric tailored to historical documents' characteristics.

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Section: Introductionmentioning

confidence: 99%

Automatic Corresponding Control Points Selection for Historical Document Image Registration

Wang

Brown

Tan

2009

2009 10th International Conference on Document Analysis and Recognition

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“…manually compare the unknown sample with all of the known samples within the database. Therefore, automated handwriting identification algorithms can be very useful by making the identification of an unknown sample of text from a large database of known writers quite fast with high confidence, (AlMaadeed, 2012;Fiel and Sablatnig, 2012;Franke and Köppen, 2001). This greatly reduces the work of the forensic analyst when comparing an unknown sample to tens of thousands of documents.…”

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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“…Writer identification can be applied by using the following two methods: the text dependent method and the text independent method. The text dependent method [7][8][9][10] is based on the writing style of character/word/lines. The text independent method [11][12][13][14] does not depend upon handwriting style.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Membership Based Writer Identification from Handwritten Devnagari Script

Kumar¹,

Ravulakollu²,

Bhat³

2015

J Inf Process Syst

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The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

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A computer-based system to support forensic studies on handwritten documents

Cited by 66 publications

References 21 publications

Automatic Corresponding Control Points Selection for Historical Document Image Registration

Automatic Corresponding Control Points Selection for Historical Document Image Registration

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

Fuzzy-Membership Based Writer Identification from Handwritten Devnagari Script

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