基于深度神经网络的烟码智能识别方法

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.