scholarly journals Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation

2020 ◽  
Vol 1575 ◽  
pp. 012065
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Mingtao Guo ◽  
Jiulong Zhang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Quality Evaluation ◽  
Chinese Character ◽  
Writing Quality ◽  
Feature Extraction Method ◽  
Gabor Feature ◽  
Gabor Feature Extraction

A FOURIER-RADIAL DESCRIPTOR ALGORITHM FOR INVARIANT FEATURE EXTRACTION

2006 ◽  
Vol 04 (01) ◽  
pp. 197-212
Author(s):  
CHALLA S. SASTRY ◽  
ARUN K. PUJARI ◽  
B. L. DEEKSHATULU
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Symmetric Functions ◽  
It Use ◽  
Feature Extraction Method ◽  
Invariant Feature ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Gabor Feature ◽  
Gabor Feature Extraction

By integrating the Fourier techniques and the edge information obtained using the radial symmetric functions, we propose in this paper an invariant feature extraction algorithm. Unlike the Gabor feature extraction method, the present method does not use direction dependent filters, nor does it use the images in polar form, for rotation invariance. Besides, the present Fourier-Radial invariant feature extraction algorithm, suitable for both the texture and non-texture images, has functional analogy with the Gabor feature extraction method, and hence, is easily implementable. It is mathematically proved, and justified through computations, that the method can generate the invariant and discriminative feature vectors. Our simulation results demonstrate that the method can be used for such applications as content-based image retrieval.

Research on Feature Extraction Method for Handwritten Chinese Character Recognition Based on Supervised Independent Component Analysis

2013 ◽  
Vol 774-776 ◽  
pp. 1636-1641
Author(s):  
Ze Min Liu ◽  
Zhi Guo He ◽  
Yu Dong Cao
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Character Recognition ◽  
Extraction Method ◽  
Chinese Character ◽  
Complex Structure ◽  
Recognition Rate ◽  
Component Analysis ◽  
Independent Component ◽  
Feature Extraction Method

Feature extraction is very difficult for handwritten Chinese character because of large Chinese characters set, complex structure and very large shape variations. The recognition rate by currently used feature extraction methods is far from the requirements of the people. For this problem, a new supervised independent component analysis (SICA) algorithm based on J-divergence entropy is proposed for feature extraction, which can measure the difference between different categories. The scheme takes full advantage of good extraction local features capability and powerful capability to handle data with non-Gaussian distribution by ICA, and the extracted feature component and classification can be tightly combined. The experiments show that the feature extraction method based on SICA is superior to that of gradient-based and that of ICA.

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