Page Object Detection with YOLOF

Abstract

With the rapid development of information and technology, document digitization has become more critical in many research fields by giving enormous amounts of data. However, computers can not handle a lot of information contained inside physical documents. For that reason, making computers detect objects in document images can help humans have more valuable information such as graphs, captions, or tables. There should be a system capable of detecting various components on document images, especially finding a simply effective object recognition method. Thus, the introduction of YOLOF can be an appropriate method to detect objects in documents because it opens up a simple way to exploit image features, making the object detection problem less computationally intensive, but still maintaining the appropriate accuracy. This paper evaluates the new one-stage YOLOF method on two challenging document datasets: IIIT-AR-13K, UIT-DODV. Our experimental YOLOF model achieves 58.8% and 56% on mAP measurement scores with the IIIT-AR-13K dataset and the UIT-DODV dataset, respectively.