基于深度神经网络的烟码智能识别方法
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摘要
卷烟条码是烟草局对卷烟是否串货销售的主要判断依据,针对当前人工录码方式操作烦琐、效率低、成本高的问题,提出一种基于深度神经网络的烟码智能识别方法.首先通过迁移学习技术构建区域检测模型,实现对烟码区域的准确定位;然后采用基于角点检测的切割算法将烟码区域切分为待识别的小块;再构建字符识别模型,对小块进行多字符识别;最后按顺序拼接各小块的识别结果输出完整烟码.实验结果表明,该方法准确率高、运行速度快,能够替代人工录码方式,满足实际应用需求.
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.
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.
引文
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[7]Yoo H J.Deep convolution neural networks in computer vision[J].IEIE Transactions on Smart Processing and Computing,2015,4(1):35-43
[8]Li Zongmin,Zhou Chenchen,Gong Yanhe,et al.Saliency object detection based on domain transform and contour detection[J].Journal of Computer-Aided Design&Computer Graphics,2018,30(8):1457-1465(in Chinese)(李宗民,周晨晨,宫延河,等.结合域变换和轮廓检测的显著性目标检测[J].计算机辅助设计与图形学学报,2018,30(8):1457-1465)
[9]Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2014:580-587
[10]Girshick R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision.Los Alamitos:IEEEComputer Society Press,2015:1440-1448
[11]He K M,Zhang X Y,Ren S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916
[12]Ren S Q,He K M,Girshick R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge:MIT Press,2015:91-99
[13]Zhang Shun,Gong Yihong,Wang Jinjun.The development of deep convolution neural network and its applications on computer vision[J].Chinese Journal of Computers,2017,40(144):1-30(in Chinese)(张顺,龚怡宏,王进军.深度卷积神经网络的发展及其在计算机视觉领域的应用[J].计算机学报,2017,40(144):1-30)
[14]Lecun Y,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324
[15]Sermanet P,Chintala S,LeCun Y.Convolutional neural networks applied to house numbers digit classification[C]//Proceedings of the 21st International Conference on Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2012:3288-3291
[16]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Cambridge:MIT Press,2012:1097-1105
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[18]Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2015:1-9
[19]Liu W Y,Wen Y D,Yu Z D,et al.Large-margin Softmax loss for convolutional neural networks[C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning.New York:ACM Press,2016:507-516
[20]Ioffe S,Szegedy C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning.New York:ACMPress,2015:448-456
[21]Nair V,Hinton G E.Rectified linear units improve restricted Boltzmann machines[C]//Proceedings of the 27th International Conference on International Conference on Machine Learning.New York:ACM Press,2010:807-814