  End-to-end scene text spotting has made significant progress due to its
intrinsic synergy between text detection and recognition. Previous methods
commonly regard manual annotations such as horizontal rectangles, rotated
rectangles, quadrangles, and polygons as a prerequisite, which are much more
expensive than using single-point. Our new framework, SPTS v2, allows us to
train high-performing text-spotting models using a single-point annotation.
SPTS v2 reserves the advantage of the auto-regressive Transformer with an
Instance Assignment Decoder (IAD) through sequentially predicting the center
points of all text instances inside the same predicting sequence, while with a
Parallel Recognition Decoder (PRD) for text recognition in parallel, which
significantly reduces the requirement of the length of the sequence. These two
decoders share the same parameters and are interactively connected with a
simple but effective information transmission process to pass the gradient and
information. Comprehensive experiments on various existing benchmark datasets
demonstrate the SPTS v2 can outperform previous state-of-the-art single-point
text spotters with fewer parameters while achieving 19$\times$ faster inference
speed. Within the context of our SPTS v2 framework, our experiments suggest a
potential preference for single-point representation in scene text spotting
when compared to other representations. Such an attempt provides a significant
opportunity for scene text spotting applications beyond the realms of existing
paradigms. Code is available at: https://github.com/Yuliang-Liu/SPTSv2.
