Multimodal Approach for Harmonized System Code Prediction
Otmane Amel, Sédrick Stassin, Sidi Ahmed Mahmoudi, Xavier Siebert · 2023
The rapid growth of e-commerce has placed considerable pressure on customs representatives, prompting advanced methods.In tackling this, Artificial intelligence (AI) systems have emerged as a promising approach to minimize the risks faced.Given that the Harmonized System (HS) code is a crucial element for an accurate customs declaration, we propose a novel multimodal HS code prediction approach using deep learning models exploiting both image and text features obtained through the customs declaration combined with e-commerce platform information.We evaluated two early fusion methods and introduced our MultConcat fusion method.To the best of our knowledge, few studies analyze the featurelevel combination of text and image in the state-of-the-art for HS code prediction, which heightens interest in our paper and its findings.The experimental results prove the effectiveness of our approach and fusion method with a top-3 and top-5 accuracy of 93.5% and 98.2% respectively.* These authors contributed equally to this work † The authors thank the support of Infortech institute and the E-origin project funded by the Walloon Region within the pole of logistics in Wallonia.1 Example of the Belgian governmental free access database of HS code nomenclature: https://eservices.minfin.fgov.be/extTariffBrowser/browseNomen.xhtml?suffix=80&lang=EN