Multimodal Approach for Harmonized System Code Prediction
<|reference_start|>Multimodal Approach for Harmonized System Code Prediction: 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<|reference_end|>
@article{amel2024multimodal,
    title={Multimodal Approach for Harmonized System Code Prediction},
    author={Otmane Amel, Sedrick Stassin, Sidi Ahmed Mahmoudi and Xavier Siebert},
    journal={Proceedings of the 31th European Symposium on Artificial Neural
  Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp.
  181-186},
    year={2024},
    doi={10.14428/esann/2023.ES2023-165},
    archivePrefix={arXiv},
    eprint={2406.04349},
    primaryClass={cs.SE cs.AI}
}