Multimodal Approach for Harmonized System Code Prediction
This addresses the challenge of accurate customs processing for e-commerce, though it appears incremental as it builds on existing multimodal methods in a specific domain.
The paper tackles the problem of predicting Harmonized System (HS) codes for customs declarations by proposing a multimodal deep learning approach that uses both image and text features, achieving top-3 and top-5 accuracies of 93.5% and 98.2% respectively.
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