Chinese Calligraphy Word Spotting Using Elastic HOG Feature and Derivative Dynamic Time Warping
Yong Xia,Zhi-Bo Yang, Kuan-Quan Wang
(School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)
Abstract:
Chinese calligraphy is a very special style of handwriting and direct character recognition is very difficult. Content-based keyword spotting is more feasible than recognition-based retrieval for calligraphy document. In this paper, we propose a novel Elastic Histogram of Oriented Gradient (EHOG) descriptor for calligraphy word spotting. The presented feature is a modification of Histogram of Oriented Gradient (HOG), widely used in human detection. In our approach, the input word image is partitioned into non-uniform rectangular cells according to the calligraphy character pixel intensity, and then in each cell a histogram of orientation is accumulated dynamically. Moreover, we adopt Derivative Dynamic Time Warping (DDTW) for image feature matching, which achieves good performance in gesture recognition. Experiments demonstrate a very significant improvement when comparing our proposed feature with previously developed ones, and also show DDTW produces superior alignments between two calligraphy character feature series than DTW.
Key words: calligraphy word spotting Elastic HOG DDTW
DOI：10.11916/j.issn.1005-9113.2014.02.004
Clc Number:TP391.4
Fund:
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Chinese Calligraphy Word Spotting Using Elastic HOG Feature and Derivative Dynamic Time Warping
本站小编 哈尔滨工业大学/2019-10-23
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