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.
作者
谢志峰
吴佳萍
章曙涵
汤臻
范杰
马利庄
Xie Zhifeng;Wu Jiaping;Zhang Shuhan;Tang Zhen;Fan Jie;and Ma Lizhuang(Department of Film and Television Engineering,Shanghai University,Shanghai 200072;Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240;Shanghai Engineering Research Center of Motion Picture Special Effects,Shanghai 200072;Monopoly Management Supervision Office,Shanghai Tobacco Monopoly Administration,Shanghai 200082;Information Center,Shanghai Tobacco Group Co,Ltd,Shanghai 200082)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2019年第1期111-117,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61303093
61402278)
上海市科委科技攻关项目(16511101300)
关键词
烟码
深度神经网络
智能识别
区域检测
字符识别
cigar-code
deep neural networks
intelligent recognition
area detection
character recognition
