Navya, B. J. and Shivakumara, P. and Shwetha, G. C. and Sangheeta Roy and Guru, D. S. and Pal, Umapada and Tong Lu (2018) Adaptive multi-Gradient Kernels for Handwritting Based Gender Identification. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR).
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Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | document image processing;feature extraction;gradient methods;handwriting recognition;text detection;writing styles;feature vector;unconstrained handwriting;Sobel kernels;extracted text lines;dominant pixels;directional symmetry;text pixels;histogram operation;adaptive multigradient values;AMGF values;adaptive multigradient feature extraction;handwriting based gender identification;adaptive multigradient kernels;Feature extraction;Kernel;Image edge detection;Writing;Histograms;Junctions;Laplace equations;Handwriting analysis, Gradient direction, Multi gradient, Adaptive Kernel, Gender identifiction |
| Subjects: | D Physical Science > Computer Science |
| Divisions: | Department of > Computer Science |
| Depositing User: | Manjula P Library Assistant |
| Date Deposited: | 08 Nov 2019 10:25 |
| Last Modified: | 08 Nov 2019 10:25 |
| URI: | http://eprints.uni-mysore.ac.in/id/eprint/9901 |
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