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Top1. Introduction
In recent years, with the rapid development of logistics and the rapid growth of logistics demand, the logistics industry is facing transformation and change on a global scale, Both the introduction of new technology, equipment and the improvement of intelligence level, have a profound impact on logistics practice. New logistics solutions are evolving to meet various needs (Islam et al. 2016, Tang et al. 2017, Xie et al. 2005, Huang et al. 2013, Han et al. 2017). Recently, soft computing and decision technology has evolved to analyze field data and generate intelligent algorithms that enables automated logistics systems to control the workflow, material flow and information flow of the global supply chain network based on IC tags, achieving intelligent logistics through the seamless integration of intelligent, decision technology and IT technology. Logistics systems have completely changed the way we manage factories, logistics, outsourcing and supply chain networks. Among them, information intelligent management technology in logistics has gradually increased its importance in China, the competition within the logistics industry is becoming increasingly fierce, each enterprise is required to build logistics distribution system with more efficient and lower cost, so as to increase enterprise benefit.
In the process of logistics, large amounts of data will be automatically transferred to bar code, and then automatic identification and classification of the bar code images on the goods can be realized, real-time information of goods transport can be obtained, which is very important to both the distribution of the cargo transport and the efficiency of logistics management. Due to its power and operability, bar code image recognition technology is the most widely used automatic recognition technology so far. The barcode image classification problem belongs to the scene character recognition problem in natural scene images. The main process of character recognition is divided into two parts, the detection and segmentation of the numbered area in the image must first be conducted. Next, the individual bar code image can be put into the character recognition system to complete the whole character recognition process. On the other hand, bar code images often appear uneven, contaminated and damaged; At the same time, since the influence of natural factors on the scene of image collection, such as the intensity of light, shooting Angle and so on, will lead to low quality of the collected image, the image must be preprocessed.