Individuality of handwriting: a validation study

Srihari, Sargur N.; Cha, Sung-Hyuk; Arora, H.; Lee, Sangjik

doi:10.1109/icdar.2001.953764

Cited by 58 publications

(34 citation statements)

References 2 publications

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“…The extraction of the feature is done with the use of macro-features algorithm (Sargur et al, 2008;Srihari et al, 2001;. Techniques provided by the Rosetta toolkit including Holte 1R classification, Genetic algorithm and Exhausive algorithm are used on the discretized and undiscretized data for the purpose of identification (Ohrn and Komorowski, 1997).…”

Section: Resultsmentioning

confidence: 99%

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Feature Discretization for Individuality Representation in Twins Handwritten Identification

Mohammed

Shamsuddin²

2011

Journal of Computer Science

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Problem statement:The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. Handwriting is one of the common types of forensic evidence. Differentiating the similarities of writing of a pair of twins is critical in establishing the reliability of handwriting identification. Writing style can be used as biometric features in authenticating individual uniqueness where these unique features can be used to identify the writer, including between a pair of twins. Existing works in Writer Identification concentrate on feature extraction and the classification task in order to identify authorship. The high similarity in a pair of twins' handwriting may degrade classification performance. There should be some standards to represent these unique features before entering into the classification task which is with the use of discretization technique. Approach: We proposed a new framework for writer identification in terms of identifying twins' handwriting and showed the effect of discretization process on handwriting samples of a pair of twins in order to obtain individual identification. Results: An experiment has been done at the Sulaimania University in Iraq with fourteen pairs of identical twins where each twin provides 4 samples of handwriting for the purpose of data collecting. These samples were implemented in this research making a comparison between the new proposed framework and classic framework. Conclusion: Our experimental results showed that with new framework identification of handwriting of a pair of twins can be improved through the discretization process.

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Section: Resultsmentioning

confidence: 99%

“…We have chosen Macro-Features because features that capture the global characteristics of the writer's individual writing habit and style can be regarded to be macro-features (Srihari et al, 2007). More details on the procedure of the macro-feature algorithm can be found in (Sargur et al, 2008;Srihari et al, 2001;.…”

Section: Individuality Of Handwritingmentioning

confidence: 99%

Feature Discretization for Individuality Representation in Twins Handwritten Identification

Mohammed

Shamsuddin²

2011

Journal of Computer Science

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“…This approach has been validated on two datasets of 711 writers using the same letter [1] achieving a classification performance of 89 and 87 %, respectively. Said et al described a text-independent writer identification method based on Gabor filtering and grayscale cooccurrence matrices.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous cases have dealt with evidence provided by handwritten documents, such as wills and ransom notes [1]. Moreover, writer identification can be used in handwriting recognition when adapting the recognizers to a specific type of writers [2] and in handwriting synthesis when generating a text as it would have been written by a specific writer [3].…”

Section: Introductionmentioning

confidence: 99%

Novel geometric features for off-line writer identification

Al-Maadeed

Hassaine

Bouridane

et al. 2014

Pattern Anal Applic

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Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain codebased features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chaincode features and edge-based directional features.

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“…Bandi et al [7] proposed a system that classifies handwritings into demographic categories using the "macro-features" introduced in [8]. These features focus on measurements such as pen pressure, writing movement, stroke formation, and word proportion.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

Al-Maadeed

Ferjani

Elloumi

et al. 2016

J Image Video Proc.

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A challenging area of pattern recognition is the recognition of handwritten texts in different languages and the reduction of a volume of data to the greatest extent while preserving associations (or dependencies) between objects of the original data. Until now, only a few studies have been carried out in the area of dimensionality reduction for handedness detection from off-line handwriting textual data. Nevertheless, further investigating new techniques to reduce the large amount of processed data in this field is worthwhile. In this paper, we demonstrate that it is important to select only the most characterizing features from handwritings and reject all those that do not contribute effectively to the process of handwriting recognition. To achieve this goal, the proposed approach is based mainly on fuzzy conceptual reduction by applying the Lukasiewicz implication. Handwritten texts in both Arabic and English languages are considered in this study. To evaluate the effectiveness of our proposal approach, classification is carried out using a K-Nearest-Neighbors (K-NN) classifier using a database of 121 writers. We consider left/right handedness as parameters for the evaluation where we determine the recall/precision and F-measure of each writer. Then, we apply dimensionality reduction based on fuzzy conceptual reduction by using the Lukasiewicz implication. Our novel feature reduction method achieves a maximum reduction rate of 83.43 %, thus making the testing phase much faster. The proposed fuzzy conceptual reduction algorithm is able to reduce the feature vector dimension by 31.3 % compared to the original "BEST OF ALL COMBINED FEATURES" algorithm.

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Individuality of handwriting: a validation study

Cited by 58 publications

References 2 publications

Feature Discretization for Individuality Representation in Twins Handwritten Identification

Feature Discretization for Individuality Representation in Twins Handwritten Identification

Novel geometric features for off-line writer identification

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

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