Name
Abstract | ||
|---|---|---|
Cigarette code is a string printed on the wrapper of cigarette packet as a basis of distinguishing illegal sales for tobacco administration. In general, the code is excerpted and entered to administration system manually during on-site inspection, which is quite time-consuming and laborious. In this paper, we propose a new solution based on convolutional neural network for intelligent transcription. Our recognition method is composed of four components: detection, identification, alignment, and regularization. First of all, the detection component fine-tunes an end-to-end detection network to obtain the bounding box region of cigarette code. Then the identification component constructs an optimized CNN architecture to recognize each character in the region of cigarette code. Meanwhile the alignment component trains a CPM-based network to estimate the positions of all characters including some missing characters. Finally, the regularization component develops a matching algorithm to produce a regularized result with all characters. The experimental results demonstrate that our proposed method can perform a better, faster and more labor-saving cigarette code transcription process. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-34120-6_20 | IMAGE AND GRAPHICS, ICIG 2019, PT I |
Keywords | DocType | Volume |
Cigarette code, Optical Character Recognition, Convolutional neural network | Conference | 11901 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhifeng Xie | 1 | 53 | 10.70 |
| Shu-Han Zhang | 2 | 0 | 0.34 |
| Peng Wu | 3 | 41 | 13.09 |