Discretization Problem for Rough Sets Methods
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Establishing Handwriting Individuality Using Pattern Recognition Techniques
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Analysis of Handwriting Individuality Using Word Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Off-line Handwriting Identification Using HMM Based Recognizers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A writer identification and verification system
Pattern Recognition Letters
Embedded scale united moment invariant for identification of handwriting individuality
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
An integrated formulation of Zernike representation in character images
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
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Writer identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in. Its focal point is in forensics and biometric application as such the writing style can be used as biometric features for authenticating a writer. Handwriting style is a personal to individual and it is implicitly represented by unique features that are hidden in individual's handwriting. These unique features can be used to identify the handwritten authorship accordingly. Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. However, this paper investigates the individuality representation of individual features through discretization technique. Discretization is a procedure to explore the partition of attributes into intervals and to unify the values for each interval. It illustrates the pattern of data systematically which improved the identification accuracy. An experiment has been conducted using IAM database with 3520 training data and 880 testing data (70% training data and 30% testing data) and 2639 training data and 1760 testing data (60% training data and 40% testing data). The results reveal that with invariants discretization, the accuracy of handwritten identification is improved significantly with the classification accuracy of 99.90% compared to undiscretized data.