On Exploring and Improving Robustness of Scene Text Detection Models

• URL: http://arxiv.org/abs/2110.05700v1
• Date: Tue, 12 Oct 2021 02:36:48 GMT
• Title: On Exploring and Improving Robustness of Scene Text Detection Models
• Authors: Shilian Wu, Wei Zhai, Yongrui Li, Kewei Wang, Zengfu Wang
• Abstract summary: We evaluate scene text detection models ICDAR2015-C (IC15-C) and CTW1500-C (CTW-C) We perform a robustness analysis of six key components: pre-training data, backbone, feature fusion module, multi-scale predictions, representation of text instances and loss function. We present a simple yet effective data-based method to destroy the smoothness of text regions by merging background and foreground.
• Score: 20.15225372544634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. For systematically exploring this problem, we propose two datasets from which to evaluate scene text detection models: ICDAR2015-C (IC15-C) and CTW1500-C (CTW-C). Our study extends the investigation of the performance and robustness of the proposed region proposal, regression and segmentation-based scene text detection frameworks. Furthermore, we perform a robustness analysis of six key components: pre-training data, backbone, feature fusion module, multi-scale predictions, representation of text instances and loss function. Finally, we present a simple yet effective data-based method to destroy the smoothness of text regions by merging background and foreground, which can significantly increase the robustness of different text detection networks. We hope that this study will provide valid data points as well as experience for future research. Benchmark, code and data will be made available at \url{https://github.com/wushilian/robust-scene-text-detection-benchmark}.

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