  We present a novel problem of text-based visual question generation or
TextVQG in short. Given the recent growing interest of the document image
analysis community in combining text understanding with conversational
artificial intelligence, e.g., text-based visual question answering, TextVQG
becomes an important task. TextVQG aims to generate a natural language question
for a given input image and an automatically extracted text also known as OCR
token from it such that the OCR token is an answer to the generated question.
TextVQG is an essential ability for a conversational agent. However, it is
challenging as it requires an in-depth understanding of the scene and the
ability to semantically bridge the visual content with the text present in the
image. To address TextVQG, we present an OCR consistent visual question
generation model that Looks into the visual content, Reads the scene text, and
Asks a relevant and meaningful natural language question. We refer to our
proposed model as OLRA. We perform an extensive evaluation of OLRA on two
public benchmarks and compare them against baselines. Our model OLRA
automatically generates questions similar to the public text-based visual
question answering datasets that were curated manually. Moreover, we
significantly outperform baseline approaches on the performance measures
popularly used in text generation literature.
