Geometrical feature based ranking using grey relational analysis (GRA) for writer identification

Jalil, Intan Ermahani A.; Muda, Azah Kamilah; Shamsuddin, Siti Mariyam; Ralescu, Anca

doi:10.1109/socpar.2013.7054118

Cited by 4 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data transformation and representation for the purpose of improving predictive accuracy are usually done to compute better input values for any classification model in any field of study when dealing with large amount of data [1]- [4]. Discretization gives the advantages of reducing and simplifying complex data by deploying the generalization factor to the datasets to improve the performance accuracy rate [5]- [6]. This is aimed to transform the representation of each data into invariant discretization that is easier to use and learn by classifiers.…”

Section: Introductionmentioning

confidence: 99%

Discretization Method to Generalize Features for Authors’ Recognition

Jalil¹,

Shamsuddin²,

Muda³

et al. 2020

IJETT

Self Cite

View full text Add to dashboard Cite

Features reconstruction or representation to improve the performance accuracy rate is a crucial procedure for handwriting image analysis in the area of authors' recognition. The process of feature extraction usually led to features redundancy with high similarity between features or classes. This will cause the problem of lower performance from classifier models that will have difficulties to differentiate the authors. This study proposes to reconstruct the formulation of discretization method of Equal Width Binning (EWB) to simplify the process. Discretization method is proposed to represent the features and infused the generalization factor into features that are being generated. This is aimed to improve the performance accuracy for authors' recognition. This study deploys the Higher-Order United Moment Invariant (HUMI) and Edge based Directional (ED) feature extraction method to generate Global and Local features respectively from the handwriting images. These generated features were reconstructed by discretization method by representing them with each unique general features. The proposed discretization method has succeeded to improve the average performance of Global Discretized Features that performs at 99.81% and Local Discretized Features achieved up until 99.89%. This shows that the discretization method has managed to improve the features' performance in determining the authors' characteristic.

show abstract

Section: Introductionmentioning

confidence: 99%

Discretization Method to Generalize Features for Authors’ Recognition

Jalil¹,

Shamsuddin²,

Muda³

et al. 2020

IJETT

Self Cite

View full text Add to dashboard Cite

show abstract

Performance Comparison of Equal Width and Equal Frequency Discretization Methods for Author’s Handwriting Recognition

Jalil

Azmi

Ahmad

et al. 2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Comparison of global and local features for author’s identification by using geometrical and zoning methods

Jalil

Shamsuddin

Muda

et al. 2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Identification analysis for author’s handwriting image in forensic investigation is still an important research area in this current big data era. Images feature extraction can lead to an issue of high dimensionality of data. The process of feature extraction is the most crucial process in author’s identification. It is important to choose the best method to represent the image. This study compared two feature extraction methods, namely Higher-Order United Moment Invariant (HUMI) and the Edge-based Directional (ED) method that construct the Global and Local Features respectively. The additional process of discretization was implemented before the training and testing phase to represent the generalized features for the classifier models. This process induced a better performance accuracy for both methods where the discretized Local Features achieved 99.95% accuracy rate that slightly outperforms the discretized Global Features with only 99.91%.

show abstract

Geometrical feature based ranking using grey relational analysis (GRA) for writer identification

Cited by 4 publications

References 27 publications

Discretization Method to Generalize Features for Authors’ Recognition

Discretization Method to Generalize Features for Authors’ Recognition

Performance Comparison of Equal Width and Equal Frequency Discretization Methods for Author’s Handwriting Recognition

Comparison of global and local features for author’s identification by using geometrical and zoning methods

Contact Info

Product

Resources

About