IEEE Trans Image Process. 2016 Jun;25(6):2789-2802. doi: 10.1109/TIP.2016.2555080. Epub 2016 Apr 15.
In this paper, we are concerned with the problem of automatic scene text recognition, which involves localizing and reading characters in natural images. We investigate this problem from the perspective of representation and propose a novel multi-scale representation, which leads to accurate, robust character identification and recognition. This representation consists of a set of mid-level primitives, termed strokelets, which capture the underlying substructures of characters at different granularities. The Strokelets possess four distinctive advantages: 1) usability: automatically learned from character level annotations; 2) robustness: insensitive to interference factors; 3) generality: applicable to variant languages; and 4) expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of the strokelets and demonstrate the effectiveness of the text recognition algorithm built upon the strokelets. Moreover, we show the method to incorporate the strokelets to improve the performance of scene text detection.
在本文中，我们关注自动场景文本识别问题，该问题涉及在自然图像中定位和读取字符。我们从表示的角度研究这个问题，并提出一种新颖的多尺度表示，它能实现准确、稳健的字符识别。这种表示由一组中级基元组成，称为笔画段，它们在不同粒度上捕捉字符的底层子结构。笔画段具有四个显著优点：1）可用性：从字符级注释中自动学习；2）稳健性：对干扰因素不敏感；3）通用性：适用于多种语言；4）表现力：能有效描述字符。在标准基准上进行的大量实验验证了笔画段的优点，并证明了基于笔画段构建的文本识别算法的有效性。此外，我们展示了将笔画段纳入以提高场景文本检测性能的方法。
