A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
This addresses the challenge of spotting keywords in Chinese text without visual blanks, which is a domain-specific problem for computer vision and OCR applications.
The paper tackles Chinese keyword spotting in natural images by proposing a method that predicts keyword masks guided by text line detection, achieving verification on two new datasets derived from RCTW-17 and ICPR MTWI2018.
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.