Semantic and Interaction: When Document Image Analysis Meets Computer Vision and Machine Learning Researchers in the DIA field have proposed different interesting techniques for the extraction of elements of contents inside images at different levels: lexical, syntactical, semantical. Besides, different knowledge representation techniques have been involved for the analysis of relations between them. From these observations, it seems very interesting to propose a comparative study showing the increasing similarity between CV and DIA problems and discussing the recent trends that have been or can be proposed in a short future to try to deal more efficiently with semantic and user interaction in these fields (computer vision and robust reading). By selecting and explaining interesting recent works from the CV and DIA communities, this paper tries to bring answers to the following questions: Can we do more than automatic features selection with CNN based architectures? Can we reach a more semantical point of view on the data? What are the solutions to define adaptable semantic models that can be easily learned or userdefined? How can we get more generic systems by combining machine learning u0026 user interaction?

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.