TY - GEN
T1 - Writing style adversarial network for handwritten Chinese character recognition
AU - Liu, Huan
AU - Lyu, Shujing
AU - Zhan, Hongjian
AU - Lu, Yue
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The performance of handwritten Chinese character recognition (HCCR) has been greatly improved by using deep learning methods in recent years. But few people pay attention to the influence of writing style on it. In this paper, we aim to improve the performance of HCCR further by weakening the influence of different writing styles. We propose a writing style adversarial network (WSAN) which includes three parts: feature extractor, character classifier and writer classifier. In the training process, we first preprocess raw image with feature extractor. Afterwards, the learned features are fed into both the character classifier and the writer classifier. We apply joint optimization on the top of these two classifiers. Specifically, we minimize the loss value of the character classifier to achieve character recognition function. At the same time, we maximize the loss value of the writer classifier to reduce the influence of writing style in HCCR. The experimental results on CASIA-HWDB1.1 prove that the proposed WSAN has a promoting effect on HCCR. And the experiments on the offline HCCR competition dataset of ICDAR-2013 also give competitive results compared with other methods.
AB - The performance of handwritten Chinese character recognition (HCCR) has been greatly improved by using deep learning methods in recent years. But few people pay attention to the influence of writing style on it. In this paper, we aim to improve the performance of HCCR further by weakening the influence of different writing styles. We propose a writing style adversarial network (WSAN) which includes three parts: feature extractor, character classifier and writer classifier. In the training process, we first preprocess raw image with feature extractor. Afterwards, the learned features are fed into both the character classifier and the writer classifier. We apply joint optimization on the top of these two classifiers. Specifically, we minimize the loss value of the character classifier to achieve character recognition function. At the same time, we maximize the loss value of the writer classifier to reduce the influence of writing style in HCCR. The experimental results on CASIA-HWDB1.1 prove that the proposed WSAN has a promoting effect on HCCR. And the experiments on the offline HCCR competition dataset of ICDAR-2013 also give competitive results compared with other methods.
KW - Gradient reversal layer
KW - Handwritten chinese character recognition
KW - Style adversarial network
UR - https://www.scopus.com/pages/publications/85089613611
U2 - 10.1007/978-3-030-36808-1_8
DO - 10.1007/978-3-030-36808-1_8
M3 - 会议稿件
AN - SCOPUS:85089613611
SN - 9783030368074
T3 - Communications in Computer and Information Science
SP - 66
EP - 74
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
Y2 - 12 December 2019 through 15 December 2019
ER -