On Image-Processing-Based Identification Method of Express Logistics Information

Abstract

As a modern comprehensive information platform for integrated statistical analysis of express shipments information and for express shipments management decision-making, the express logistics track and trace system needs to use artificial intelligence (AI) technology and image processing technology to automatically extract the text content of express logistics documents. Existing express shipments information identification models usually have problems such as less-than-ideal performance in detecting single characters or small text regions of express logistics documents, high human resource cost for character-level markup, and low speed and accuracy of text recognition. In response, this paper studies the image-processing-based identification method of express logistics information. It presents a recognition process for pre-processing text images of express logistics documents, along with a detailed description of denoising, greyscaling and binarisation methods. While proposing an enhancement strategy for Chinese characters in the section of handwritten Chinese, this paper constructs a model for recognition of express shipping document texts based on bidirectional long-short term memory (LSTM) and attention mechanism. In this way, we fully mined key semantic information of express logistics document texts. The experimental results verify the effectiveness of the constructed model.