# Scene Text Visual Question Answering Source: [Scene Text Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Ali Furkan Biten, Ruben Tito, Andres Mafla, Lluis Gomez, Mar\\c{c}al Rusi\\~nol, Ernest Valveny, C.V. Jawahar, Dimosthenis Karatzas Published: 2019-05-31T14:47:55.000Z ## Abstract Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()