@inproceedings{0ea90746abff4e7cacc9f2b52272727b,
title = "Chinese Writer Identification Using Contour-Directional Feature and Character Pair Similarity Measurement",
abstract = "The key issue of Chinese writer identification is the uncertainty of the text content in the query and reference handwriting images. We propose a method for Chinese writer identification using Contour-directional Feature (CDF) and Character Pair Similarity Measurement (CPSM). CDFs are extracted from the query and reference handwriting images and are used to calculate the text-independent similarity between the query and reference handwriting images. Meanwhile, characters appearing in both the query and reference handwriting images are also utilized to measure the similarity of character pairs. The text-independent similarity and the similarity of character pairs are fused to the final similarity between the query and reference handwriting images. The proposed method is evaluated on two public datasets. The best Top-1 identification accuracy on the HIT-MW and CASIA-2.1 dataset reaches 96.7\% and 97.9\% respectively, which outperforms other previous approaches.",
keywords = "Character pair similarity measurement, Chinese writer identification, Contour-directional feature, Keypoint matching, Similarity fusion",
author = "Xiong, \{Yu Jie\} and Lu Yue",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 ; Conference date: 09-11-2017 Through 15-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICDAR.2017.28",
language = "英语",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
publisher = "IEEE Computer Society",
pages = "119--124",
booktitle = "Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017",
address = "美国",
}