@inproceedings{ffecc276b3254b5992f0175f8d270912,
title = "HOG based two-directional Dynamic Time Warping for handwritten word spotting",
abstract = "We present a Histogram of Oriented Gradient (HoG) based two-directional Dynamic Time Warping (DTW) matching method for handwritten word spotting. Firstly, we extract HoG descriptors from each cell in the normalized images. Then we connect the HoG descriptors in the same column and get a sequence of feature vectors. We do the same operation for the HoG descriptors in the same row. We then apply the two-directional DTW method to calculate the distance between the feature vectors sequences extracted from the query word and the candidate one. The experimental results show that the two-directional DTW is more robust to word deformation than the traditional DTW. And the local features such as HoG, LBP and SIFT combined with the two-directional DTW method outperform the method using the local feature descriptors directly. The HoG based two-directional DTW get the highest mean average precision on both the George Washington dataset and the CASIA-HWDB 2.1 dataset.",
keywords = "handwritten documents, local features, two-directional Dynamic Time Warping, word spotting",
author = "Shunyi Yao and Ying Wen and Yue Lu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 13th International Conference on Document Analysis and Recognition, ICDAR 2015 ; Conference date: 23-08-2015 Through 26-08-2015",
year = "2015",
month = nov,
day = "20",
doi = "10.1109/ICDAR.2015.7333744",
language = "英语",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
publisher = "IEEE Computer Society",
pages = "161--165",
booktitle = "13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings",
address = "美国",
}