@inproceedings{582ca60d101d4fa2858f5640a6ee697e,
title = "One-Shot Chinese Character Recognition Based on Deep Siamese Networks",
abstract = "This paper applies deep siamese network to one-shot Chinese handwritten character recognition. Different from common image classification tasks, the CASIA HWDB1.1 dataset used here contains more than 3000 categories, with only few samples in each one. We propose a basic deep siamese model as well as an improved model with multi-layer features mechanism and batch normalization for extracting the similarity of the input pairs, and implement one-shot recognition by categorizing the test example to the class where the support sample is the most similar. Experiments prove that our model is able to recognize Chinese characters of unseen classes in training with only one support example efficiently.",
keywords = "CASIA dataset, Convolutional neural network, One-shot learning, Siamese network",
author = "Huichao Li and Xuemei Ren and Yongfeng Lv",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2019 ; Conference date: 26-10-2019 Through 27-10-2019",
year = "2020",
doi = "10.1007/978-981-32-9682-4\_78",
language = "English",
isbn = "9789813296817",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "742--750",
editor = "Yingmin Jia and Junping Du and Weicun Zhang",
booktitle = "Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I",
address = "Germany",
}