Morphological waveform coding for writer identification
TL;DR: Both Bayesian classifiers and neural networks are employed to test the efficiency of the proposed feature and the achieved identification success using a long word exceeds 95%.
About: This article is published in Pattern Recognition.The article was published on 2000-03-01 and is currently open access. It has received 166 citations till now. The article focuses on the topics: Feature vector & Feature (computer vision).
Citations
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TL;DR: New and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality are developed.
Abstract: The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates
468 citations
Cites background from "Morphological waveform coding for w..."
...Writer identification is rooted in the older and broader domain of automatic handwriting recognition [1], [2]....
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TL;DR: It is shown that both the writer identification and the writer verification tasks can be carried out using local features such as graphemes extracted from the segmentation of cursive handwriting, making the approach general and very promising for large scale applications in the domain of handwritten document querying and writer verification.
208 citations
Cites background from "Morphological waveform coding for w..."
...We now give full details of each processing step of our writer identification system....
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...The work presented in (Zois and Anastassopoulos, 2000) reports a correct writer identification performance of 92,48% among 50 writers by using 45 samples of the same word that the participants were asked to write....
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TL;DR: An effective method for automatic writer recognition from unconstrained handwritten text images based on the presence of redundant patterns in the writing and its visual attributes is proposed, which exhibits promising results on writer identification and verification.
204 citations
10 Sep 2001
TL;DR: From handwritten lines of text, twelve features are extracted which are used to recognize persons, based on their handwriting, which mainly correspond to visible characteristics of the writing, for example, the width, the slant and the height of the three main writing zones.
Abstract: We present a system for writer identification. From handwritten lines of text, twelve features are extracted which are used to recognize persons, based on their handwriting. The features extracted mainly correspond to visible characteristics of the writing, for example, the width, the slant and the height of the three main writing zones. Additionally, features based on the fractal behavior of the writing, which are correlated with the writing's legibility, are used. With these features two classifiers are applied: a k-nearest neighbor and a feedforward neural network classifier. In the experiments, 100 pages of text written by 20 different writers are used. By classifying individual text lines, an average recognition rate of 87.8% for the k-nearest neighbor and 90.7% for the neural network is measured. By a simple maximum ranking over all lines of a page, all texts are correctly assigned to the corresponding writers. Compared to these results, an average recognition rate of 98% was measured when humans assigned persons to the text lines.
166 citations
TL;DR: Through a series of comprehensive experiments, this work shows that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points, and the proposed approach using LPQ features is able to achieve accuracies of 96.7% and 99.2% on the BFL and IAM and databases respectively.
Abstract: Highlights? A segmentation free process for writer identification/verification. ? Evaluation of two texture descriptors (LBP and LPQ) for writer identification/verification. ? Evaluation of the dissimilarity-based approach for writer identification. ? Discussion about the number and size of the references for the dissimilarity-based approach. In this work, we discuss the use of texture descriptors to perform writer verification and identification. We use a classification scheme based on dissimilarity representation, which has been successfully applied to verification problems. Besides assessing two texture descriptors (local binary patterns and local phase quantization), we also address important issues related to the dissimilarity representation, such as the impact of the number of references used for verification and identification, how the framework performs on the problem of writer identification, and how the dissimilarity-based approach compares to other feature-based strategies. In order to meet these objectives, we carry out experiments on two different datasets, the Brazilian forensic letters database and the IAM database. Through a series of comprehensive experiments, we show that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points. For the identification problem, the proposed approach using LPQ features is able to achieve accuracies of 96.7% and 99.2% on the BFL and IAM and databases respectively.
157 citations
Additional excerpts
...X2 97.7 91.0 Kirli and Gulmezoglu (2011) IAM 2011 Global and Local 93 NDDF – 98.7 Said et al. (2000) – 1998 Gabor e GLCM 40 WED – 96.0 Zois and Anastassopoulos (2000) – 1999 Morphological 50 MLP – 96.5 Cha and Srihari (2000) – 2002 Micro and Macro 1000 k-NN – 81.0 Shen et al. (2002) – 2002 Texture…...
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...X2 97.7 91.0 Kirli and Gulmezoglu (2011) IAM 2011 Global and Local 93 NDDF – 98.7 Said et al. (2000) – 1998 Gabor e GLCM 40 WED – 96.0 Zois and Anastassopoulos (2000) – 1999 Morphological 50 MLP – 96.5 Cha and Srihari (2000) – 2002 Micro and Macro 1000 k-NN – 81.0 Shen et al. (2002) – 2002 Texture 50 k-NN – 97.6 He and Tang (2004) – 2004 Gabor 50 WED – 97.0 Ubul et al. (2009) – 2009 Gabor and ICA 55 k-NN – 92.5 Ours BFL 2012 Texture (LPQ) 315 SVM 99.4 99.2 Ours IAM 2012 Texture (LPQ) 650 SVM 99.6 96.7 the queried document, but, after feature extraction, all that variability is lumped into the same feature vector....
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References
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46,339 citations
"Morphological waveform coding for w..." refers background in this paper
...(15) and (17) becomes after some mathematical manipulations [ 24 ]...
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17,845 citations
"Morphological waveform coding for w..." refers background or methods in this paper
...The statistics of the feature components and their agreement with the normal density is examined by means of the statistical "t tests [20], and especially the Kolmogorov}Smirnov (K}S) test [21]....
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...Under certain conditions and given that hypothesis H 0 is true, the Kolmogorov}Smirnov statistic D follows the cumulative distribution [21]...
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14,948 citations
Book•
01 Jan 1965
TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Abstract: Part 1 Probability and Random Variables 1 The Meaning of Probability 2 The Axioms of Probability 3 Repeated Trials 4 The Concept of a Random Variable 5 Functions of One Random Variable 6 Two Random Variables 7 Sequences of Random Variables 8 Statistics Part 2 Stochastic Processes 9 General Concepts 10 Random Walk and Other Applications 11 Spectral Representation 12 Spectral Estimation 13 Mean Square Estimation 14 Entropy 15 Markov Chains 16 Markov Processes and Queueing Theory
13,886 citations
Book•
01 Jan 1973
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
13,647 citations