  Text detection and recognition are essential components of a modern OCR
system. Most OCR approaches attempt to obtain accurate bounding boxes of text
at the detection stage, which is used as the input of the text recognition
stage. We observe that when using tight text bounding boxes as input, a text
recognizer frequently fails to achieve optimal performance due to the
inconsistency between bounding boxes and deep representations of text
recognition. In this paper, we propose Box Adjuster, a reinforcement
learning-based method for adjusting the shape of each text bounding box to make
it more compatible with text recognition models. Additionally, when dealing
with cross-domain problems such as synthetic-to-real, the proposed method
significantly reduces mismatches in domain distribution between the source and
target domains. Experiments demonstrate that the performance of end-to-end text
recognition systems can be improved when using the adjusted bounding boxes as
the ground truths for training. Specifically, on several benchmark datasets for
scene text understanding, the proposed method outperforms state-of-the-art text
spotters by an average of 2.0% F-Score on end-to-end text recognition tasks and
4.6% F-Score on domain adaptation tasks.
