论文标题
由文本线检测指导的多个导向的中文关键字片段
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
论文作者
论文摘要
中文关键字发现是一项具有挑战性的任务，因为中文单词没有视觉空白。与通过视觉空白自然划分的英语单词不同，中文单词通常仅由语义信息分开。在本文中，我们提出了一个新的中文关键字来介绍自然图像，该词灵感来自Mask R-CNN。我们建议预测以文本线检测为指导的关键字蒙版。首先，文本行的建议是由更快的R-CNN生成的；然后，通过建议中的细分来预测文本行掩模和关键字掩码。通过这种方式，文本线和关键字可以并行预测。我们基于RCTW-17和ICPR MTWI2018创建两个中文关键字数据集，以验证我们方法的有效性。
Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words. Different from English words which are split naturally by visual blanks, Chinese words are generally split only by semantic information. In this paper, we propose a new Chinese keyword spotter for natural images, which is inspired by Mask R-CNN. We propose to predict the keyword masks guided by text line detection. Firstly, proposals of text lines are generated by Faster R-CNN;Then, text line masks and keyword masks are predicted by segmentation in the proposals. In this way, the text lines and keywords are predicted in parallel. We create two Chinese keyword datasets based on RCTW-17 and ICPR MTWI2018 to verify the effectiveness of our method.
