  This paper presents final results of ICDAR 2019 Scene Text Visual Question
Answering competition (ST-VQA). ST-VQA introduces an important aspect that is
not addressed by any Visual Question Answering system up to date, namely the
incorporation of scene text to answer questions asked about an image. The
competition introduces a new dataset comprising 23,038 images annotated with
31,791 question/answer pairs where the answer is always grounded on text
instances present in the image. The images are taken from 7 different public
computer vision datasets, covering a wide range of scenarios.
  The competition was structured in three tasks of increasing difficulty, that
require reading the text in a scene and understanding it in the context of the
scene, to correctly answer a given question. A novel evaluation metric is
presented, which elegantly assesses both key capabilities expected from an
optimal model: text recognition and image understanding.
  A detailed analysis of results from different participants is showcased,
which provides insight into the current capabilities of VQA systems that can
read. We firmly believe the dataset proposed in this challenge will be an
important milestone to consider towards a path of more robust and general
models that can exploit scene text to achieve holistic image understanding.
