Omnidirectional scene text detection has received increasing research attention. Previous methods directly predict words or text lines of quadrilateral shapes. However, most methods neglect the significance of consistent labeling, which is important to maintain a stable training process, especially when a large amount of data are included. For the first time, we solve the problem in this paper by proposing a novel method termed Sequential-free Box Discretization (SBD). The proposed SBD first discretizes the quadrilateral box into several key edges, which contains all potential horizontal and vertical positions. In order to decode accurate vertex positions, a simple yet effective matching procedure is proposed to reconstruct the quadrilateral bounding boxes. It departs from the learning ambiguity which has a significant influence during the learning process. Exhaustive ablation studies have been conducted to quantitatively validate the effectiveness of our proposed method. More importantly, built upon SBD, we provide a detailed analysis of the impact of a collection of refinements, in the hope to inspire others to build state-of-the-art networks. Combining both SBD and these useful refinements, we achieve state-of-the-art performance on various benchmarks, including ICDAR 2015, and MLT. Our method also wins the first place in text detection task of the recent ICDAR2019 Robust Reading Challenge on Reading Chinese Text on Signboard, further demonstrating its powerful generalization ability. Code is available at https://tinyurl.com/sbdnet.

本文提出了无序框离散化（OBD）方法，该方法通过将四边形框离散为多个包含所有潜在水平和垂直位置的关键边，解决了一些文本检测方法忽略一致标注的问题，此方法通过大量实验验证了其有效性，同时提出的改进有助于构建最先进的文本检测器，最终在ICDAR2015和MLT等各种基准测试中取得了最新的最优表现。

探索无序盒子离散化网络在多方向场景文本检测中的能力