Intelligent Recognition Method for Cigarette Code Based on Deep Neural Networks

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.