  Most existing scene text detectors focus on detecting characters or words
that only capture partial text messages due to missing contextual information.
For a better understanding of text in scenes, it is more desired to detect
contextual text blocks (CTBs) which consist of one or multiple integral text
units (e.g., characters, words, or phrases) in natural reading order and
transmit certain complete text messages. This paper presents contextual text
detection, a new setup that detects CTBs for better understanding of texts in
scenes. We formulate the new setup by a dual detection task which first detects
integral text units and then groups them into a CTB. To this end, we design a
novel scene text clustering technique that treats integral text units as tokens
and groups them (belonging to the same CTB) into an ordered token sequence. In
addition, we create two datasets SCUT-CTW-Context and ReCTS-Context to
facilitate future research, where each CTB is well annotated by an ordered
sequence of integral text units. Further, we introduce three metrics that
measure contextual text detection in local accuracy, continuity, and global
accuracy. Extensive experiments show that our method accurately detects CTBs
which effectively facilitates downstream tasks such as text classification and
translation. The project is available at
https://sg-vilab.github.io/publication/xue2022contextual/.
