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Graduate Students from the Department of Electronic Engineering, Tsinghua University won in Competition on Text Detection and Recognition in Arabic News Video Frames hosted by ICPR

Recently, the 25th International Conference on Pattern and Recognition (ICPR) was held online. The results of the third Competition on Text Detection and Recognition in Arabic News Video Frames (AcTiVComp) were announced, and Ph. D. candidate YAN Ruijie and Master candidate XIAO Shanyu, etc. advised by Associate Professor PENG Liangrui from the Department of Electronic Engineering at Tsinghua University were champions in both tasks of text detection and text recognition.

AcTiVComp was organized by the HES-SO University of Applied Sciences and Arts of Western Switzerland, the University of Fribourg in Switzerland, and the University of Sousse in Tunisia. Participants came from China, Switzerland, Malaysia, Norway, India and Pakistan, etc.

The award certificate in Arabic text detection.

The award certificate in Arabic text recognition.

Multilingual OCR (Optical Character Recognition) technologies including Arabic OCR are crucial in global information communication and utilization, which are also the cutting-edge research topics in machine learning and artificial intelligence research fields. Advised by PENG Liangrui, the graduate students participated in the AcTiVComp include YAN Ruijie, XIAO Shanyu, YAO Gang and Shi Haodong. The text detection algorithm was mainly developed by XIAO Shanyu, which has novel contributions in deep learning model architecture and multi-task supervised learning; the text recognition algorithm was mainly developed by YAN Ruijie, which has breakthroughs in efficient feature representation learning and transfer learning. PENG Liangrui’s research group has previously won in both tasks of text detection and recognition in the AcTivComp hosted by ICDAR (International Conference on Document Analysis and Recognition) in 2017. Compared with the algorithms in 2017, the newest algorithms have achieved significant performance improvements in both text detection and text recognition.

The related research work was supported by National Key R&D Program of China, the Institute for Guo Qiang at Tsinghua University, and Beijing National Research Center for Information Science and Technology.