Improving Data Quality in the Cargo Industry with Modern Recurrent Neural Network Architecture

Abstract

The shipping industry is a multifaceted trading system, enabling the flow of goods between countries. Often the standards, methods and technologies become interspersed and difficult to plan, disrupting the supply chain. Data is not always accurate, necessitating further analysis. This paper presents an approach to solving key issues in two important facets of the supply chain, predicting the date of restitution for a cargo container and using an optical character recognition (OCR)-centred pipeline to extrapolate data from containers. Both approaches use long short-term memory (LSTM) models, including bi-directional LSTMs. These methods leverage state-of-the-art text recognition architecture and advanced algorithm ensembling, giving significant improvements in data quality. The experimental results illustrate that these methods vastly outperform current industry practices and more recent approaches.