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....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.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61173086)Shandong Excellent Young Scientist Award Fund(Grant No.BS2011DX002)Shandong Province Science and Technology Development Planning(Grant No.2012GSF12105)
文摘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.
