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.

翻译：典型的文本定位器遵循两阶段定位范式，即先检测文本实例的边界，再在检测区域内进行文本识别。尽管此类定位范式取得了显著进展，但其重要局限在于文本识别的性能高度依赖文本检测的精度，从而导致由检测向识别的潜在误差传播。在本文中，我们提出单发自洽场景文本定位器v2（SRSTS v2），通过将识别与检测解耦并协同优化两项任务来规避这一局限。具体而言，我们的SRSTS v2在每个潜在文本实例周围采样具有代表性的特征点，并引导这些采样点并行执行文本检测与识别。因此，文本识别不再依赖检测，从而缓解了从检测到识别的误差传播。此外，采样模块在检测和识别的联合监督下进行学习，使得两项任务能够协同优化并相互增强。得益于这种采样驱动的并发定位框架，即使在精确文本边界难以检测的情况下，我们的方法也能正确识别文本实例。在四个基准数据集上的大量实验表明，我们的方法优于最先进的文本定位器。