A statistical model for writer verification

Srihari, Sargur N.; Beal, Matthew J.; Bandi, K.; Shah, Vivek; Krishnamurthy, P.

doi:10.1109/icdar.2005.33

Cited by 39 publications

(21 citation statements)

References 4 publications

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“…Often, a technique is dedicated to a certain language. In [1] the terms micro and macro features were introduced. Micro features represent fine characteristics, e. g., on character level like grapheme-based approaches [2].…”

Section: Introductionmentioning

confidence: 99%

Writer Identification for Historical Arabic Documents

Fecker

Asit

Märgner

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Identification of writers of handwritten historical documents is an important and challenging task. In this paper we present several feature extraction and classification approaches for the identification of writers in historical Arabic manuscripts. The approaches are able to successfully identify writers of multipage documents. The feature extraction methods rely on different principles, such as contour-, textural-and key point-based and the classification schemes are based on averaging and voting. For all experiments a dedicated data set based on a publicly available database is used. The experiments show promising results and the best performance was achieved using a novel feature extraction based on key point descriptors.

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Section: Introductionmentioning

confidence: 99%

Writer Identification for Historical Arabic Documents

Fecker

Asit

Märgner

et al. 2014

2014 22nd International Conference on Pattern Recognition

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“…6 The Wanda system provides a flexible workbench for performing document examination and writer-identification tasks. In Trigraph, modern user-interface technology is combined with (i) expert knowledge from forensic experts, (ii) automatically derived image features computed from a scanned handwritten document, 1,3,27,32 and (iii) information based on allographic character features. 40 This paper focuses on the latter issue.…”

Section: Introductionmentioning

confidence: 99%

Automatic Allograph Matching in Forensic Writer Identification

Niels

Vuurpijl

Schomaker

2007

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

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A well-established task in forensic writer identification focuses on the comparison of prototypical character shapes (allographs) present in handwriting. In order for a computer to perform this task convincingly, it should yield results that are plausible and understandable to the human expert. Trajectory matching is a well-known method to compare two allographs. This paper assesses a promising technique for so-called humancongruous trajectory matching, called Dynamic Time Warping (DTW). In the first part of the paper, an experiment is described that shows that DTW yields results that correspond to the expectations of human users. Since DTW requires the dynamics of the handwritten trace, the "online" dynamic allograph trajectories need to be extracted from the "offline" scanned documents. In the second part of the paper, an automatic procedure to perform this task is described. Images were generated from a large online dataset that provides the true trajectories. This allows for a quantitative assessment of the trajectory extraction techniques rather than a qualitative discussion of a small number of examples. Our results show that DTW can significantly improve the results from trajectory extraction when compared to traditional techniques.

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“…The font classifier is then applied first to a test field, and its decision is used to select the appropriate character classifier [40,41,42,43]. The same idea can be applied to writer identification [44]. Many words of text may be necessary to reliably identify the font.…”

Section: Font Classificationmentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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A statistical model for writer verification

Cited by 39 publications

References 4 publications

Writer Identification for Historical Arabic Documents

Writer Identification for Historical Arabic Documents

Automatic Allograph Matching in Forensic Writer Identification

Adaptive and Interactive Approaches to Document Analysis

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