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.
@article{arxiv.2001.00722,
title = {A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection},
author = {Pei Xu and Shan Huang and Hongzhen Wang and Hao Song and Shen Huang and Qi Ju},
journal= {arXiv preprint arXiv:2001.00722},
year = {2020}
}