Intelligent Recognition Method for Cigarette Code Based on Deep Neural Networks

被引：0
|
作者：
Xie Z. [1 ,3 ]
Wu J. [1 ]
Zhang S. [1 ]
Tang Z. [4 ]
Fan J. [5 ]
Ma L. [2 ,3 ]
机构：
[1] Department of Film and Television Engineering, Shanghai University, Shanghai
[2] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai
[3] Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai
[4] Monopoly Management Supervision Office, Shanghai Tobacco Monopoly Administration, Shanghai
[5] Information Center, Shanghai Tobacco Group Co, Ltd, Shanghai
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 01期
关键词：
Area detection; Character recognition; Cigar-code; Deep neural networks; Intelligent recognition;
D O I：
10.3724/SP.J.1089.2019.17319
中图分类号：
学科分类号：
摘要：
Cigarette identification code is the basis of discrimination of illegal retailing for tobacco boards, yet it's artificial transcription was quite costly and inefficient. In this paper, we proposed a high-efficient and accurate cigar-code identification method based on Deep Neural Network (DNN). First, it utilized Transfer Learning technology for constructing regional detection model to locate the cigar-code region precisely. Then, it divided the region into small blocks by a cutting algorithm based on Corner Detection. Afterwards, it constructed a character recognition model for multi-character recognition of the small blocks. At last, it reordered the recognition results to achieve a full cigar-code. Results show that our DNN-based cigar-code identification method achieves high accuracy and is far more efficient than artificial transcription, which meets the practical application requirements. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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收藏
页码：111 / 117
页数：6
相关论文
共 21 条
  • [1] Wang Q.Q., Lu Y., Sun S.L., Text detection in nature scene images using two-stage nontext filtering, Proceedings of the 13th International Conference on Document Analysis and Recognition, pp. 106-110, (2015)
  • [2] Gouk H.G.R., Blake A.M., Fast Sliding Window Classification with Convolutional Neural networks, Proceedings of the 29th International Conference on Image and Vision Computing, pp. 114-118, (2014)
  • [3] Ye Q.X., Doermann D., Text detection and recognition in imagery: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 7, pp. 1480-1500, (2015)
  • [4] Sedaghat A., Mokhtarzade M., Ebadi H., Uniform robust scale-invariant feature matching for optical remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 49, 11, pp. 4516-4527, (2011)
  • [5] Bay H., Tuytelaars T., van Gool L., SURF: speeded up robust features, Proceedings of the 9th European Conference on Computer Vision, pp. 404-417, (2006)
  • [6] Zhu Q., Yeh M.C., Cheng K.T., Et al., Fast human detection using a cascade of histograms of oriented gradients, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, pp. 1491-1498, (2006)
  • [7] Yoo H.J., Deep convolution neural networks in computer vision, IEIE Transactions on Smart Processing and Computing, 4, 1, pp. 35-43, (2015)
  • [8] Li Z., Zhou C., Gong Y., Et al., Saliency object detection based on domain transform and contour detection, Journal of Computer-Aided Design & Computer Graphics, 30, 8, pp. 1457-1465, (2018)
  • [9] Girshick R., Donahue J., Darrell T., Et al., Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 580-587, (2014)
  • [10] Girshick R., Fast R-CNN, Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, (2015)
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