Abstract
Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection methods. However, recent researches reveal that introducing quadrilateral bounding box for scene text detection will bring a label confusion issue which is easily overlooked, and this issue may significantly undermine the detection performance. To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance. Experiments showed that the proposed method can outperform state-of-the-art methods in many popular scene text benchmarks, including ICDAR 2015, MLT, and MSRA-TD500. Ablation study also showed that simply integrating the SBD into Mask R-CNN framework, the detection performance can be substantially improved. Furthermore, an experiment on the general object dataset HRSC2016 (multi-oriented ships) showed that our method can outperform recent state-of-the-art methods by a large margin, demonstrating its powerful generalization ability.
Abstract (translated)
野外场景文本通常呈现出高度变异的特征。使用四边形边界框对文本实例进行局部化是检测方法中必不可少的。然而，近年来的研究表明，引入四边形边界框进行场景文本检测会带来容易被忽视的标签混淆问题，这一问题可能会严重影响检测性能。针对这一问题，本文提出了一种将边界盒离散为关键边缘的序列自由盒离散化方法，进一步推导出提高检测性能的有效方法。实验表明，该方法在许多流行的场景文本基准测试（包括ICDAR 2015、MLT和MSRA-TD500）中优于最先进的方法。烧蚀研究还表明，简单地将SBD集成到掩模R-CNN框架中，可以显著提高检测性能。此外，在通用对象数据集HRSC2016（多方位船）上进行的一次实验表明，我们的方法在很大程度上优于目前最先进的方法，证明了其强大的泛化能力。
URL
https://arxiv.org/abs/1906.02371
