Image classification of customs procedure documents using machine learning and deep learning models

The images of documents used in customs procedures were classified using machine learning and deep learning models. As image classification methods, Gaussian Naive Bayes, Random Forest, and Support Vector Machine were used in the machine learning field, and Convolutional Neural Network, Vision Transformer, ConvNext and EfficientNetV2 were used in the deep learning field. Datasets were organized specifically for each model type, and models were created with parameter values determined to ensure the models worked at the optimum level. Training and testing were conducted with the relevant datasets. The models were compared in terms of training times and accuracy rates. The best results were achieved with Convolutional Neural Network, which had a training time of approximately 11 minutes and an accuracy rate of 98,32%; second-best accuracy result achieved with Random Forest, which had an accuracy rate of 97,94% and second-best overall results achieved with EfficientNetV2, which had a training time of approximately 22 minutes and an accuracy rate of 95,89%.