Calligraphy Word Style Recognition by KNN Based Feature Library Filtering

DOI

10.2991/icmt-13.2013.115How to use a DOI?

Keywords

Calligraphy words·Writing style recognition·Feature vector filtering·Digital library

Abstract

Chinese calligraphy works is a valuable part of the Chinese culture heritage. More and more calligraphy works are digitized, preserved and exhibited in digital library so that people can enjoy the calligraphers’ works conveniently. There are five main writing style categories of calligraphy words, namely, seal script, clerical script, standard script, semi-cursive script and cursive script. Users always want to appreciate the style-similar works simultaneously, so it’s necessary to classify the words by their writing style. In this study, we proposed a method based on KNN and feature vector filtering. Firstly, extract the SIFT points from the training images, building up a feature library for SIFT feature vectors. Then use a KNN-based method to filter the feature library so that the style-irrelevant feature points can be wiped out. At last we use the filtered feature library to classify the word images by a modified KNN classifier. Experiments show that SIFT feature has better recognition result than that of Gabor feature and GIST feature, but the large amount of feature vectors in the SIFT feature library makes the KNN searching rather slow. To accelerate the recognition speed, Spectral Hashing is used to index the feature library, which makes it faster to classify feature points and gives no side effect on the recognition ratio.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mao Tianjiao
AU  - Wu Jiangqin
AU  - Gao Pengcheng
AU  - Xia Yang
AU  - Lin Yuan
PY  - 2013/11
DA  - 2013/11
TI  - Calligraphy Word Style Recognition by KNN Based Feature Library Filtering
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 927
EP  - 934
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.115
DO  - 10.2991/icmt-13.2013.115
ID  - Tianjiao2013/11
ER  -