  Typical text spotters follow the two-stage spotting paradigm which detects
the boundary for a text instance first and then performs text recognition
within the detected regions. Despite the remarkable progress of such spotting
paradigm, an important limitation is that the performance of text recognition
depends heavily on the precision of text detection, resulting in the potential
error propagation from detection to recognition. In this work, we propose the
single shot Self-Reliant Scene Text Spotter v2 (SRSTS v2), which circumvents
this limitation by decoupling recognition from detection while optimizing two
tasks collaboratively. Specifically, our SRSTS v2 samples representative
feature points around each potential text instance, and conducts both text
detection and recognition in parallel guided by these sampled points. Thus, the
text recognition is no longer dependent on detection, thereby alleviating the
error propagation from detection to recognition. Moreover, the sampling module
is learned under the supervision from both detection and recognition, which
allows for the collaborative optimization and mutual enhancement between two
tasks. Benefiting from such sampling-driven concurrent spotting framework, our
approach is able to recognize the text instances correctly even if the precise
text boundaries are challenging to detect. Extensive experiments on four
benchmarks demonstrate that our method compares favorably to state-of-the-art
spotters.
