IEEE Trans Image Process. 2014 Jul;23(7):2972-82. doi: 10.1109/TIP.2014.2317980.
Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.
自然场景中的文本字符和字符串可为许多应用提供有价值的信息。由于文本模式多样且背景干扰多样，直接从自然场景图像或视频中提取文本是一项具有挑战性的任务。本文提出了一种从检测到的文本区域中识别场景文本的方法。在文本检测中，我们应用之前提出的算法从场景图像中获取文本区域。首先，我们设计了一种判别字符描述符，通过结合几种最先进的特征检测器和描述符来实现。其次，我们通过设计笔画配置图来对每个字符类的字符结构进行建模。我们的算法设计与智能移动设备上的场景文本提取应用兼容。我们开发了一个基于 Android 的演示系统，以展示我们提出的从附近物体中提取场景文本信息的方法的有效性。该演示系统还为我们提供了一些关于场景文本提取的算法设计和性能改进的见解。在基准数据集上的评估结果表明，我们提出的文本识别方案可与现有最佳方法相媲美。
