  Image text carries essential information to understand the scene and perform
reasoning. Text-based visual question answering (text VQA) task focuses on
visual questions that require reading text in images. Existing text VQA systems
generate an answer by selecting from optical character recognition (OCR) texts
or a fixed vocabulary. Positional information of text is underused and there is
a lack of evidence for the generated answer. As such, this paper proposes a
localization-aware answer prediction network (LaAP-Net) to address this
challenge. Our LaAP-Net not only generates the answer to the question but also
predicts a bounding box as evidence of the generated answer. Moreover, a
context-enriched OCR representation (COR) for multimodal fusion is proposed to
facilitate the localization task. Our proposed LaAP-Net outperforms existing
approaches on three benchmark datasets for the text VQA task by a noticeable
margin.
