A neural-network-based framework for cigarette laser code identification
A neural-network-based framework for cigarette laser code identification
复制标题
基于神经网络的香烟激光代码识别框架
DOI：
10.1007/s00521-019-04647-2
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发表时间：
2019-12-02
影响因子：
6
通讯作者：
Zheng, Xu
中科院分区：
文献类型：
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作者：
Yang, Zeheng;Xie, Xiurui;Zheng, Xu
卷烟激光条码识别是鉴别烟草真伪的重要手段。然而，由于卷烟图像背景复杂，现有的字符识别方法在卷烟识别中的应用受到限制。为了解决这个问题，本文提出了一种新的基于神经网络的框架。具体而言，该框架包括三个主要步骤。首先，设计了一种主元分析神经网络用于倾斜校正过程，以克服强噪声干扰。然后提出了一种新的算法，自适应地利用先验分割信息更好地分割字符。最后，设计了一个CNN模型来提取字符识别的不规则特征。通过这样做，所提出的框架的影响，不同的背景和保持有用的功能在同一时间。此外，我们给出了基于所提出的方法的字符识别的洞察力分析。在由100张香烟激光编码照片组成的图像集上对该框架的性能进行了评估，其结果表明，与基线方法相比，我们的框架可以带来约30%的识别精度提高。良好的性能表明了我们的框架在实际应用中的巨大潜力。
The identification of cigarette laser codes is important in distinguishing the authenticity of tobacco. However, the existing character recognition methods have limited use in the identification due to the complex background in cigarette images. To address this issue, we propose a novel neural-network-based framework in this paper. Specifically, the framework includes three major steps. Firstly, a principal component analysis neural network is designed for the inclination correction progress to overcome the strong noise interferences. Then a novel algorithm is proposed to adaptively utilize the prior partition information for better character segmentation. Finally, a CNN model is designed to extract irregular features for character identification. By doing this, the proposed framework alleviates the influence of diverse backgrounds and keeps useful features at the same time. Additionally, we give an insight analysis on the character recognition based on the proposed method. The performance of the framework is evaluated on an image set composed of 100 cigarette laser code photos, whose results demonstrate that our framework can bring about 30% improvement in recognition accuracy compared to baseline methods. The good performance indicates a huge potential of our framework on practical applications.