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.
翻译：中文关键词定位是一项具有挑战性的任务,因为中文字眼上没有可见空白。 与自然由视觉空白分割的英文词不同, 中文通常只由语义信息分割。 在本文中, 我们提议为自然图像设置一个新的中文关键词识别器, 由Mask R- CNN 启发。 我们提议通过文本线探测来预测关键字掩码。 首先, 快速 R- CNN 生成了文本线的建议; 然后, 文本线掩码和关键字面具通过建议中的分割来预测。 这样, 文本行和关键字可以平行预测。 我们根据 RCTW-17 和 ICPR MTWI2018 创建了两个中国关键字数据集, 以验证我们方法的有效性 。
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