Comparison of text-image fusion models for high school diploma certificate classification

File scanned documents are commonly used in this digital era. Text and image extraction of scanned documents play an important role in acquiring information. A document may contain both texts and images. A combination of text-image classification has been previously investigated. The dataset used for those research works the text were digitally provided. In this research, we used a dataset of high school diploma certificate in which the text must be acquired using optical character recognition (OCR) method. There were two categories for this high school diploma certificate, each of which has three classes. We used convolutional neural network for both text and image classifications. We then combined those two models by using adaptive fusion model and weight fusion model to find the best fusion model. We came into conclusion that the performance of weight fusion model which is 0.927 is better than that of adaptive fusion model with 0.892. © 2020 KIPMI.