OCRVQA: Visual Question Answering by Reading Text in Images The problem of answering questions about an image is popularly known as visual question answering (or VQA in short). It is a wellestablished problem in computer vision. However, none of the VQA methods currently utilize the text often present in the image. These texts in images provide additional useful cues and facilitate better understanding of the visual content. In this paper, we introduce a novel task of visual question answering by reading text in images, i.e., by optical character recognition or OCR. We refer to this problem as OCRVQA. To facilitate a systematic way of studying this new problem, we introduce a largescale dataset, namely OCRVQA200K. This dataset comprises of 207,572 images of book covers and contains more than 1 million questionanswer pairs about these images. We judiciously combine wellestablished techniques from OCR and VQA domains to present a novel baseline for OCRVQA200K. The experimental results and rigorous analysis demonstrate various challenges present in this dataset leaving ample scope for the future research. We are optimistic that this new task along with compiled dataset will openup many exciting research avenues both for the document image analysis and the VQA communities.

ICDAR 2019 Competition on Scene Text Visual Question Answering This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (STVQA). STVQA 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.