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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Detecting Uyghur text in complex background images with convolutional neural network
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| 英文论文题目: |
Detecting Uyghur text in complex background images with convolutional neural network
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| 论文题目英文: |
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| 作者: |
Fang, Shancheng
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| 论文出处: |
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| 刊物名称: |
MULTIMEDIA TOOLS AND APPLICATIONS
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| 年: |
2017
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| 卷: |
76 |
| 期: |
13 |
| 页: |
15083-15103 |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
Uyghur text detection is crucial to a variety of real-world applications, while little researches put their attention on it. In this paper, we develop an effective and efficient region-based convolutional neural network for Uyghur text detection in complex background images. The characteristics of the network include: (1) Three region proposal networks are used to improve the recall, which simultaneously utilize feature maps from different convolutional layers. (2) The overall architecture of our network is in the form of fully convolutional network, and global average pooling is applied to replace the fully connected layers in the classification and bounding box regression layers. (3) To fully utilize the baseline information, Uyghur text lines are detected directly by the network in an end-to-end fashion. Experiment results on benchmark dataset show that our method achieves an F-measure of 0.83 and detection time of 0.6 s for each image in a single K20c GPU, which is much faster than the state-of-the-art methods while keeps competitive accuracy. |
| 英文摘要: |
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