Abstract: 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.