Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts. Each Chinese character contains its specific types and combination of text structure components, which is rarely seen in backgrounds. Thus, different from the features only applicable to one text extraction stage (text detection or text recognition), the text structure component feature is suitable for both Chinese text detection and recognition. A text structure component detector (TSCD) layer is designed to detect the large amount of component types, which is the most challenging part of extracting text structure component features. Through statistical classification various types of text structure component are detected by their specially designed convolutional units in the TSCD layer. With the TSCD layer, the CNN has improvements in the accuracy and uniqueness of text feature description. In the evaluation, both text detection and recognition algorithms based on the proposed text structure feature extractor achieve state-of-the-art results in two datasets.
|Title of host publication||2016 23rd International Conference on Pattern Recognition, ICPR|
|Number of pages||6|
|Publication status||Published - 13 Apr 2017|
|Event||23rd International Conference on Pattern Recognition: ICPR 2016 - Cancun, Mexico|
Duration: 4 Dec 2016 → 8 Dec 2016
|Conference||23rd International Conference on Pattern Recognition|
|Period||4/12/16 → 8/12/16|