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摘要: 离线笔迹鉴别在司法鉴定与历史文档分析中有重要作用.当前的主要离线笔迹鉴别都是基于局部特征提取的方法, 其在笔迹检索中严重依赖于数据增强和全局编码, 在笔迹识别中需要较多的笔迹信息.针对这一问题, 本文提出一种基于统计的文档行分割与深度卷积神经网络相结合的离线笔迹鉴别方法(DLS-CNN).首先, 使用基于统计的文档行分割方法将笔迹材料分割成小的像素块; 然后, 用优化后的残差神经网络作为识别模型; 最后, 对局部特征使用取均值法进行编码.在ICDAR2013和CVL这两个标准数据集上的实验结果表明, 该方法能有效获得鲁棒的局部特征, 从而仅需要少量的笔迹信息就能取得较高的识别率, 而且不需依赖于数据增强和全局编码就能取得较好的检索效果.实验代码地址:https://github.com/shiming-chen/DLS-CNN.Abstract: Off-line writer identification plays an important role in forensics and historical document analysis. The current well-known off-line writer identification approaches are based on local feature extraction. They rely heavily on data augmentation and global encoding for writer retrieval, and need a great number of written contents for writer recognition. This paper proposes a new off-line writer identification method, called DLS-CNN, which combines document line segmentation in terms of statistic and deep convolutional neural network. More precisely, handwriting documents are segmented into patches using document line segmentation at first. Secondly, an improved residual neural network serves as the identification model. Finally, the mean value of all local features vectors are used as final global features for writer identification. Experimental results on ICADAR2013 and CVL benchmark datasets show that, due to the extracted robust local features, DLS-CNN achieves higher identification rate with fewer written contents, and better retrieval result without data augmentation and global encoding. All the experiment codes are available at https://github.com/shiming-chen/DLS-CNN.
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Key words:
- Writer identification /
- writer retrieval /
- document line segmentation /
- convolutional neural network (CNN) /
- features extraction
1) 本文责任编委 金连文 -
表 1 ResNet-50结构
Table 1 The structure of ResNet-50
Layer name Layers Output size Conv1 7 $\times$ 7, 64, Stride 2 112 $\times$ 112 Conv2-x1 3 $\times$ 3 Max pool, Stride 2 56 $\times$ 56 Conv2-x2 $\left[\begin{array}{c} 1 \times 1, 64\\ 3 \times 3, 64 \\ 1 \times 1, 256\end{array}\right] \times 3$ 56 $\times$ 56 Conv3-x $\left[\begin{array}{c} 1 \times 1, 128 \\ 3 \times 3, 128 \\ 1 \times 1, 512\end{array}\right] \times 4$ 28 $\times$ 28 Conv4-x $\left[\begin{array}{c} 1 \times 1, 256 \\ 3 \times 3, 256 \\ 1 \times 1, 1 024 \end{array}\right] \times 6$ 14 $\times$ 14 Conv5-x $\left[\begin{array}{c} 1 \times 1, 512 \\ 3 \times 3, 512 \\ 1 \times 1, 2 048 \end{array}\right] \times 3$ 7 $\times$ 7 Global average pool 1 $\times$ 1 Fc, Relu, Dropout, Softmax 1 $\times$ 1 
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表 2 不同像素块大小的对比(%)
Table 2 Comparison of different patch sizes (%)
S-1 S-5 S-10 H-2 H-3 mAP 64尺度 87.8 94.7 97.0 57.3 36.7 76.5 256尺度 $\textbf{95.0}$ $\textbf{98.4}$ $\textbf{99.3}$ $\textbf{70.3}$ $\textbf{49.5}$ $\textbf{84.7}$
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表 3 不同特征层的对比(%)
Table 3 Comparison of different feature layers (%)
S-1 S-5 S-10 H-2 H-3 mAP 全局池化层 $\textbf{95.4}$ 97.9 98.5 63.1 41.2 79.7 全连接层 95.0 $\textbf{98.4}$ $\textbf{99.3}$ $\textbf{70.3}$ $\textbf{49.5}$ $\textbf{84.7}$
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表 4 特征数目的对比(%)
Table 4 Comparison of feature numbers (%)
S-1 S-5 S-10 H-2 H-3 mAP 128 95.2 $\textbf{98.7}$ 99.0 70.1 48.6 84.3 512 95.0 98.4 $\textbf{99.3}$ $\textbf{70.3}$ $\textbf{49.5}$ $\textbf{84.7}$ 1 024 95.0 98.4 99.0 70.0 48.8 84.1 2 048 $\textbf{96.0}$ 98.4 98.6 67.1 45.8 83.0
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表 5 PCA白化的评估(%)
Table 5 Evaluation of PCA$\_$Whitening (%)
S-1 S-5 S-10 H-2 H-3 mAP 无PCA白化 88.9 97.1 98.0 63.9 47.6 82.1 有PCA白化 $\textbf{95.0}$ $\textbf{98.4}$ $\textbf{99.3}$ $\textbf{70.3}$ $\textbf{49.5}$ $\textbf{84.7}$
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表 6 与其他模型的对比(%)
Table 6 Comparison with other models (%)
S-1 S-5 S-10 H-2 H-3 mAP CS-UMD-a[3] 95.1 98.6 99.1 19.6 7.1 N/A CS-UMD-b[3] 95.0 98.6 99.2 20.2 8.4 N/A HIT-ICG[3] 94.8 98.0 98.3 63.2 36.5 N/A TEBESSA-a[3] 90.3 96.7 98.3 58.2 33.2 N/A TEBESSA-b[3] 93.4 97.8 98.5 62.6 36.5 N/A Christlein[11] 97.1 98.8 99.1 42.8 23.8 67.7 Wu[9] 95.6 98.6 99.1 63.8 36.5 N/A Nicolaou[14] $\textbf{97.2}$ $\textbf{98.9}$ 99.2 52.9 29.2 N/A Fiel[8] 88.5 96.0 98.3 40.5 15.8 N/A Christlein[24] 86.8 N/A N/A N/A N/A 78.9 DLS-CNN 95.0 98.4 $\textbf{99.3}$ $\textbf{70.3}$ $\textbf{49.5}$ $\textbf{84.7}$
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