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.
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This work was supported by the National Natural Science Foundation of China (Grant Nos. 61806040, 61771098), the China Postdoctoral Science Foundation (Grant No. 2018M633348), and the fund from the Department of Science and Technology of Sichuan Province (Grant Nos. 2017GFW0128, 18ZDYF2268, 2018JY0578 and 2017JY0007).
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Yang, Z., Xie, X., Zhan, Q. et al. A neural-network-based framework for cigarette laser code identification. Neural Comput & Applic 32, 11597–11606 (2020). https://doi.org/10.1007/s00521-019-04647-2
- Laser code identification
- Character segmentation
- Inclination correction