  RRPN is among the outstanding scene text detection approaches, but the
manually-designed anchor and coarse proposal refinement make the performance
still far from perfection. In this paper, we propose RRPN++ to exploit the
potential of RRPN-based model by several improvements. Based on RRPN, we
propose the Anchor-free Pyramid Proposal Networks (APPN) to generate
first-stage proposals, which adopts the anchor-free design to reduce proposal
number and accelerate the inference speed. In our second stage, both the
detection branch and the recognition branch are incorporated to perform
multi-task learning. In inference stage, the detection branch outputs the
proposal refinement and the recognition branch predicts the transcript of the
refined text region. Further, the recognition branch also helps rescore the
proposals and eliminate the false positive proposals by the jointing filtering
strategy. With these enhancements, we boost the detection results by $6\%$ of
F-measure in ICDAR2015 compared to RRPN. Experiments conducted on other
benchmarks also illustrate the superior performance and efficiency of our
model.
