![]() ![]() Hao Ni and Lianwen Jin*, SegCTC: Offline Handwritten Chinese Text Recognition Cheng Jian, Lianwen Jin*, Lingyu LiangĪnd Chongyu Liu, HisDoc R-CNN: Robust Chinese Historical Document Text Lineĭetection with Dynamic Rotational Proposal Network and Iterative Attention Head,ĩ. Li Zhuoming, Peng Fan, Xue Yang, Ni HaoĪnd Jin Lianwen, Scene Table Structure Recognition with Segmentation and KeyĨ. Multi-Annotation Category Dataset for Modern Document Layout Analysis, CVPRħ. Hiuyi Cheng, Sihang Wu, Peirong Zhang, Jiaxin Zhang, Qiyuan Zhu, Xiezecheng Xie, Jing Li, Kai Ding, Lianwen Jin*, M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Tampered Text Detection in Document Image: New dataset and New Solution, CVPRĦ. Xinhong Chen, Dezhi Peng, Fengjun Guo, Lianwen Jin*, Towards Robust Recognition with segmentation collaboration and alignment , Pattern Recognition Letters, 2023. Hongyi Wang, Yang Xue, Jiaxin Zhang, Lianwen Jin , Scene table structure Lianwen Jin*, Yichao Huang, Kai Ding, Improving Handwritten MathematicalĮxpression Recognition via Similar Symbol Distinguishing, IEEE Transactions on Multimediaģ. Wentao Yang, Yongxin Shi, Qing Jiang, and Lianwen Jin, A Robust and EfficientĪlgorithm for Chinese Historical Document Analysis and Recognition, National Science Review, 2023. Of Computer Vision/Image Processing/Artificialġ. He received the New Century Excellent Talent Program of MOEĪward and the Guangdong Pearl River Distinguished Professor Award in 2006 and Research interests include Optical Character Recognition, Document AnalysisĪnd Recognition, Scene Text Detection and Recognition, Computer Vision, Machine Information Engineering at the South China University of Technology. He is a professor in School of Electronic and The University of Science and Technology of China, Anhui, China, and the Ph.D.ĭegree from the South China University of Technology, Guangzhou, China, in 1991Īnd 1996, respectively. Of Electronics and Information Engineering Of Lab of Deep Learning and Vision Computing ![]()
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