Scene text recognition in mobile applications by character descriptor and structure configuration.
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.
Chucai
C
Yi
Yingli
Y
Tian
D000465:Algorithms; D057225:Data Mining; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D063731:Mobile Applications; D010363:Pattern Recognition, Automated / Q000379:methods*; D014956:Writing