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It is crucial to understand the robustness of text detection models withregard to extensive corruptions, since scene text detection techniques havemany practical applications. For systematically exploring this problem, wepropose two datasets from which to evaluate scene text detection models:ICDAR2015-C (IC15-C) and CTW1500-C (CTW-C). Our study extends the investigationof the performance and robustness of the proposed region proposal, regressionand segmentation-based scene text detection frameworks. Furthermore, we performa robustness analysis of six key components: pre-training data, backbone,feature fusion module, multi-scale predictions, representation of textinstances and loss function. Finally, we present a simple yet effectivedata-based method to destroy the smoothness of text regions by mergingbackground and foreground, which can significantly increase the robustness ofdifferent text detection networks. We hope that this study will provide validdata points as well as experience for future research. Benchmark, code and datawill be made available at\url{https://github.com/wushilian/robust-scene-text-detection-benchmark}.

On Exploring and Improving Robustness of Scene Text Detection Models