Automatic Tariff Classification System using Deep Learning

Abstract

The tariff fraction is the universal form of identi-fying a product. It is very useful because it helps to know the tariff that the product must pay when entering or leaving the country, in this case Mexico. Coffee is a complicated product to identify correctly due to its variants, which at first glance are not distinguishable, which can cause confusion and the tariff to be charged incorrectly. Therefore, the main objective of this project was to develop a system based on Deep Learning models, which allow to identify the tariff code of coffee to import or export this product through the analysis of digital images in real time, generating automatically a general report with this information for the customs broker. The developed system allows speeding up the process of assigning the tariff fraction, and also allows the correct assignment of the tariff fraction, avoiding confusion with other products and the wrong collection of the tariff. It is important to mention that the system, although for the moment it is focused on the country of Mexico, can be used in all customs offices since the tariff fraction is universal. The evaluation of the models was carried out with cross-validation, obtaining an effectiveness of more than 80%, and the tariff fraction assignment model had an effectiveness of 90%.