论文标题
RRPN ++：针对更准确的场景文本检测的指导
RRPN++: Guidance Towards More Accurate Scene Text Detection
论文作者
论文摘要
RRPN是出色的现场文本检测方法之一，但是手动设计的锚和粗提案的改进使得表演远非完美。在本文中，我们建议RRPN ++通过多种改进来利用基于RRPN的模型的潜力。基于RRPN，我们建议无锚金字塔提案网络（APPN）生成第一阶段建议，该提案采用无锚固设计以减少提案编号并加速推理速度。在我们的第二阶段，检测分支和识别分支都合并为进行多任务学习。在推理阶段，检测分支输出提案完善，识别分支预测了精制文本区域的转录本。此外，识别分支还有助于取消提案，并通过连接过滤策略消除误报提案。通过这些增强功能，与RRPN相比，ICDAR2015中的F-Measure $ 6 \％$提高了检测结果。在其他基准上进行的实验也说明了我们模型的卓越性能和效率。
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.
