  Text-based visual question answering (VQA) requires to read and understand
text in an image to correctly answer a given question. However, most current
methods simply add optical character recognition (OCR) tokens extracted from
the image into the VQA model without considering contextual information of OCR
tokens and mining the relationships between OCR tokens and scene objects. In
this paper, we propose a novel text-centered method called RUArt (Reading,
Understanding and Answering the Related Text) for text-based VQA. Taking an
image and a question as input, RUArt first reads the image and obtains text and
scene objects. Then, it understands the question, OCRed text and objects in the
context of the scene, and further mines the relationships among them. Finally,
it answers the related text for the given question through text semantic
matching and reasoning. We evaluate our RUArt on two text-based VQA benchmarks
(ST-VQA and TextVQA) and conduct extensive ablation studies for exploring the
reasons behind RUArt's effectiveness. Experimental results demonstrate that our
method can effectively explore the contextual information of the text and mine
the stable relationships between the text and objects.
