Writer Identification Based on Local Contour Distribution Feature

Ding, Hong; Wu, Huiqun; Zhang, Xiaofeng; Chen, Jianping

doi:10.14257/ijsip.2014.7.1.16

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The method operated at three stages. Ding et al [13] proposed a method based on local contour features. The distribution of local contour was extracted from the fragments, which were parts of the contour in sliding windows.…”

Section: B Related Workmentioning

confidence: 99%

Writer Verification Using Spatial Domain Features under Different Ink Width Conditions

Kore¹,

Apte²

2016

Journal of Computing Science and Engineering

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In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

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Section: B Related Workmentioning

confidence: 99%

Writer Verification Using Spatial Domain Features under Different Ink Width Conditions

Kore¹,

Apte²

2016

Journal of Computing Science and Engineering

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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.

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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%.

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