We are pleased to announce that Prof. Cheng-Lin Liu from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, China will give a talk about document analysis and recognition research. You are all invited. Registration in advance is required. #codh10
|Date||15:30-17:30, March 11 (Mon), 2019|
|Venue||1208 Meeting Room (12F), National Institute of Informatics. Access to NII.|
|15:00||Open the venue|
|15:30-15:40||Kuzushiji Challenge: Public Datasets and Machine Learning for Old Japanese Characters||Asanobu Kitamoto (CODH/NII)|
|15:40-16:00||N2I project: Recognizing Modern Japanese Magazines with Deep Learning||Anh Le Duc (CODH/ISM)|
|16:00-17:00||Advances in Document Analysis and Recognition Research at NLPR||Cheng-Lin Liu (National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, China）|
|Title||Advances in Document Analysis and Recognition Research at NLPR|
|Abstract||In this talk, I will introduce some recent advances in Document Analysis and Recognition research at the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences. Oriented to the analysis and recognition of document images of complex layout or background interference, I will mainly introduces our techniques in layout analysis of handwritten documents, scene text detection, text line recognition, classifier learning and adaptation. Our layout analysis method is based on full convolutional network (FCN) and conditional random field (CRF). For scene text detection, we proposed a deep direct regression based method for multi-oriented texts and a local region based method for end-to-end detection and recognition of arbitrary shape texts. For text line recognition, we promoted the over-segmentation based method with deep learning models, and proposed a sliding character model based method which performs superiorly for both scene texts and handwriting of different scripts. For classifier learning for document recognition, we are developing algorithms for designing models for open world recognition, small sample learning and adaptation. Last, I will introduce a new database of historical handwritten Chinese characters. This database contains more than 2.2 million character samples of 9,630 categories, segmented from ancient books and Buddist scriptures. The database have large variation of writing style and sample number per class, and can facilitate research for classifier learning and adaptation, aimed to solve the challenges of huge category set, large variation and small sample size.|
|Bio||Cheng-Lin Liu is a Professor at the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, Beijing, China, and is now the director of the laboratory. He received the B.S. degree in electronic engineering from Wuhan University, Wuhan, China, the M.E. degree in electronic engineering from Beijing Polytechnic University, Beijing, China, the Ph.D. degree in pattern recognition and intelligent control from the Chinese Academy of Sciences, Beijing, China, in 1989, 1992 and 1995, respectively. He was a postdoctoral fellow at Korea Advanced Institute of Science and Technology (KAIST) and later at Tokyo University of Agriculture and Technology from March 1996 to March 1999. From 1999 to 2004, he was a research staff member and later a senior researcher at the Central Research Laboratory, Hitachi, Ltd., Tokyo, Japan. His research interests include pattern recognition, image processing, neural networks, machine learning, and especially the applications to character recognition and document analysis. He has published over 200 technical papers at prestigious international journals and conferences. He won the IAPR/ICDAR Young Investigator Award of 2005. He is an associate editor-in-chief of Pattern Recognition Journal, an associate editor of Image and Vision and Computing, International Journal on Document Analysis and Recognition, and Cognitive Computation. He is a Fellow of the IAPR and the IEEE.|
You are all invited, free of charge. Registration in advance is required using the following form.
The seminar has fininshed. Thank you for your participation.
This seminar is supported by ROIS-DS-JOINT 027RP2018. About ROIS-DS-JOINT, see Collaboration.