Steel Delivery Order Recognition Based on Deep Learning and Posterior Error Correction Technology

Abstract

The main voucher for the circulation of bulk goods is the delivery order. The traditional warehousing enterprise receives the goods through manual input, which is inefficient and error-prone. The recognition rate of the traditional algorithm model is low and cannot be applied on a large scale. According to the characteristics of steel delivery order, through the integration of algorithm technology, this paper proposes an algorithm model based on image correction, text location, text recognition, and post verification, which solves the problem of the low recognition rate of the traditional algorithm. The character recognition rate of the recognition model is more than 95%. Finally, a visual manual correction function is developed to ensure 100% accuracy of output text data. Based on the intelligent identification technology of delivery orders, the traditional goods circulation mode is transformed from the series process of offline circulation based on traditional paper documents to the parallel process with an information-sharing cloud platform as the carrier. We build an intelligent information management system of delivery order of bulk goods is constructed. The business practice shows that the system can quickly and accurately extract the text information of delivery documents and effectively improve the efficiency of goods warehousing and circulation.