Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China.
PLoS One. 2013 Aug 5;8(8):e70173. doi: 10.1371/journal.pone.0070173. Print 2013.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.
自然场景中的文本包含丰富的语义信息，可用于辅助各种应用，例如对象识别、图像/视频检索、制图/导航和人机交互。然而，现有的大多数系统都是为检测和识别水平（或接近水平）文本而设计的。由于移动计算设备和应用的日益普及，在较少受控条件下从自然图像中检测不同方向的文本已成为一项重要但具有挑战性的任务。在本文中，我们提出了一种新的算法来检测不同方向的文本。我们的算法基于两级分类方案和两组专门设计的特征，用于捕获文本的内在特征。为了更好地评估所提出的方法并将其与竞争算法进行比较，我们生成了一个包含各种类型文本的综合数据集，这些文本来自不同的真实场景。我们还提出了一种新的评估协议，该协议更适合用于基准测试不同方向文本检测算法。在基准数据集上的实验表明，当处理水平文本时，我们的系统优于最先进的算法，并且在复杂自然场景中的变体文本方面性能显著提高。
