Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2225-2232.DOI: 10.11772/j.issn.1001-9081.2023071018
• Multimedia computing and computer simulation • Previous Articles Next Articles
Wu XIONG1,2, Congjun CAO1,2(), Xuefang SONG1,2, Yunlong SHAO1,2, Xusheng WANG1,2
Received:
2023-07-27
Revised:
2023-09-19
Accepted:
2023-09-25
Online:
2023-10-26
Published:
2024-07-10
Contact:
Congjun CAO
About author:
XIONG Wu, born in 1998, M. S. candidate. His research interests include handwriting identification, data hiding.Supported by:
熊武1,2, 曹从军1,2(), 宋雪芳1,2, 邵云龙1,2, 王旭升1,2
通讯作者:
曹从军
作者简介:
熊武(1998—),男,陕西商洛人,硕士研究生,主要研究方向:笔迹鉴别、信息隐藏;基金资助:
CLC Number:
Wu XIONG, Congjun CAO, Xuefang SONG, Yunlong SHAO, Xusheng WANG. Handwriting identification method based on multi-scale mixed domain attention mechanism[J]. Journal of Computer Applications, 2024, 44(7): 2225-2232.
熊武, 曹从军, 宋雪芳, 邵云龙, 王旭升. 基于多尺度混合域注意力机制的笔迹鉴别方法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2225-2232.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023071018
数据集 | 方法 | 准确率 | FAR | FRR |
---|---|---|---|---|
Cedar | Texture Feature* | 92.10 | 7.70 | 8.20 |
Two-stage SiamNet | 95.66 | 4.20 | 6.78 | |
SigNet | 100.00 | 0.00 | 0.00 | |
DeepHSV | 100.00 | 0.00 | 0.00 | |
MMDANet | 100.00 | 0.00 | 0.00 | |
Bengla | Texture Feature* | 76.68 | 24.19 | 24.47 |
SigNet | 86.11 | 13.56 | 13.89 | |
DeepHSV | 88.08 | 11.64 | 11.92 | |
Two-stage SiamNet | 90.64 | 8.52 | 8.86 | |
MMDANet | 91.57 | 8.21 | 8.43 | |
Hindi | Texture Feature* | 75.53 | 24.67 | 24.94 |
SigNet | 84.64 | 15.07 | 15.36 | |
DeepHSV | 86.60 | 13.06 | 13.34 | |
Two-stage SiamNet | 88.98 | 11.24 | 11.47 | |
MMDANet | 90.47 | 9.18 | 9.53 |
Tab. 1 Comparison of proposed method with state-of-the-art methods on different datasets
数据集 | 方法 | 准确率 | FAR | FRR |
---|---|---|---|---|
Cedar | Texture Feature* | 92.10 | 7.70 | 8.20 |
Two-stage SiamNet | 95.66 | 4.20 | 6.78 | |
SigNet | 100.00 | 0.00 | 0.00 | |
DeepHSV | 100.00 | 0.00 | 0.00 | |
MMDANet | 100.00 | 0.00 | 0.00 | |
Bengla | Texture Feature* | 76.68 | 24.19 | 24.47 |
SigNet | 86.11 | 13.56 | 13.89 | |
DeepHSV | 88.08 | 11.64 | 11.92 | |
Two-stage SiamNet | 90.64 | 8.52 | 8.86 | |
MMDANet | 91.57 | 8.21 | 8.43 | |
Hindi | Texture Feature* | 75.53 | 24.67 | 24.94 |
SigNet | 84.64 | 15.07 | 15.36 | |
DeepHSV | 86.60 | 13.06 | 13.34 | |
Two-stage SiamNet | 88.98 | 11.24 | 11.47 | |
MMDANet | 90.47 | 9.18 | 9.53 |
数据集 | 方法 | 准确率 | FAR | FRR |
---|---|---|---|---|
Cedar | VGG | 97.02 | 2.92 | 3.04 |
VGG + IECA | 98.78 | 1.22 | 1.34 | |
VGG + MSPM | 100.00 | 0.00 | 0.00 | |
VGG + MDM | 100.00 | 0.00 | 0.00 | |
VGG + PANet | 100.00 | 0.00 | 0.00 | |
VGG + MDM + PANet | 100.00 | 0.00 | 0.00 | |
Bengla | VGG | 81.90 | 18.10 | 18.10 |
VGG + IECA | 83.78 | 16.22 | 16.22 | |
VGG + MSPM | 86.60 | 13.40 | 13.40 | |
VGG + MDM | 89.96 | 11.01 | 9.07 | |
VGG + PANet | 88.46 | 11.54 | 11.54 | |
VGG + MDM + PANet | 91.57 | 8.21 | 8.43 | |
Hindi | VGG | 79.80 | 21.07 | 21.20 |
VGG + IECA | 82.48 | 17.21 | 17.52 | |
VGG + MSPM | 86.37 | 13.35 | 13.63 | |
VGG + MDM | 88.98 | 11.02 | 11.32 | |
VGG + PANet | 87.46 | 12.23 | 12.54 | |
VGG + MDM + PANet | 90.47 | 9.18 | 9.53 |
Tab. 2 Ablation experimental results of different modules under different datasets
数据集 | 方法 | 准确率 | FAR | FRR |
---|---|---|---|---|
Cedar | VGG | 97.02 | 2.92 | 3.04 |
VGG + IECA | 98.78 | 1.22 | 1.34 | |
VGG + MSPM | 100.00 | 0.00 | 0.00 | |
VGG + MDM | 100.00 | 0.00 | 0.00 | |
VGG + PANet | 100.00 | 0.00 | 0.00 | |
VGG + MDM + PANet | 100.00 | 0.00 | 0.00 | |
Bengla | VGG | 81.90 | 18.10 | 18.10 |
VGG + IECA | 83.78 | 16.22 | 16.22 | |
VGG + MSPM | 86.60 | 13.40 | 13.40 | |
VGG + MDM | 89.96 | 11.01 | 9.07 | |
VGG + PANet | 88.46 | 11.54 | 11.54 | |
VGG + MDM + PANet | 91.57 | 8.21 | 8.43 | |
Hindi | VGG | 79.80 | 21.07 | 21.20 |
VGG + IECA | 82.48 | 17.21 | 17.52 | |
VGG + MSPM | 86.37 | 13.35 | 13.63 | |
VGG + MDM | 88.98 | 11.02 | 11.32 | |
VGG + PANet | 87.46 | 12.23 | 12.54 | |
VGG + MDM + PANet | 90.47 | 9.18 | 9.53 |
特征提取网络 | 准确率 | FAR | FRR |
---|---|---|---|
VGG | 70.10 | 29.60 | 30.20 |
VGG + IECA | 78.06 | 20.96 | 21.84 |
VGG + MSPM | 79.41 | 20.16 | 21.02 |
VGG + MDM | 82.16 | 17.72 | 17.96 |
VGG + PANet | 80.76 | 18.12 | 20.36 |
VGG + MDM + PANet | 84.25 | 15.47 | 16.03 |
Tab. 3 Ablation experimental results of different modules on CHD dataset
特征提取网络 | 准确率 | FAR | FRR |
---|---|---|---|
VGG | 70.10 | 29.60 | 30.20 |
VGG + IECA | 78.06 | 20.96 | 21.84 |
VGG + MSPM | 79.41 | 20.16 | 21.02 |
VGG + MDM | 82.16 | 17.72 | 17.96 |
VGG + PANet | 80.76 | 18.12 | 20.36 |
VGG + MDM + PANet | 84.25 | 15.47 | 16.03 |
训练集 | 测试集 | |||
---|---|---|---|---|
Cedar | Bengla | Hindi | CHD | |
Cedar | 100.00 | 64.80 | 57.37 | 50.50 |
Bengla | 80.45 | 91.57 | 68.48 | 51.47 |
Hindi | 75.87 | 66.79 | 90.47 | 53.56 |
CHD | 55.67 | 56.78 | 55.74 | 84.25 |
Tab. 4 Identification accuracy across datasets
训练集 | 测试集 | |||
---|---|---|---|---|
Cedar | Bengla | Hindi | CHD | |
Cedar | 100.00 | 64.80 | 57.37 | 50.50 |
Bengla | 80.45 | 91.57 | 68.48 | 51.47 |
Hindi | 75.87 | 66.79 | 90.47 | 53.56 |
CHD | 55.67 | 56.78 | 55.74 | 84.25 |
MDM模块数 | 不同数据集中的准确率/% | ||
---|---|---|---|
CHD | Hindi | Bengla | |
0 | 70.10 | 79.80 | 81.90 |
1 | 80.74 | 84.90 | 86.10 |
2 | 81.06 | 86.40 | 89.45 |
3 | 82.16 | 88.98 | 89.96 |
Tab. 5 Ablation experimental results of different numbers of MDM
MDM模块数 | 不同数据集中的准确率/% | ||
---|---|---|---|
CHD | Hindi | Bengla | |
0 | 70.10 | 79.80 | 81.90 |
1 | 80.74 | 84.90 | 86.10 |
2 | 81.06 | 86.40 | 89.45 |
3 | 82.16 | 88.98 | 89.96 |
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