基于3D卷积的图像序列特征提取与自注意力的车牌识别方法
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曾淦雄, 柯逍
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3D convolution-based image sequence feature extraction and self-attention for license plate recognition method
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Ganxiong ZENG, Xiao KE
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表3 PKUData和CLPD数据集上的实验结果
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| 方法 | PKUData：ACC | PKUData：ACC w/o | CLPD：ACC | CLPD：ACC w/o | | Sighthound (2017)[46] | - | 89.3% | - | 85.2% | | TE2E[7] | 77.6% | 78.4% | 66.5% | 78.9% | | ANet (real data only)[11] | 84.8% | 86.5% | 70.8% | 86.1% | | ANet (real+synthetic data)[11] | 88.2% | 90.5% | 76.8% | 87.6% | | T-LPR | | | | |
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