@techreport{oai:ipsj.ixsq.nii.ac.jp:00216947,
 author = {Huy, Quang Ung and Hung, Tuan Nguyen and Cuong, Tuan Nguyen and Tsunenori, Ishioka and Masaki, Nakagawa and Huy, Quang Ung and Hung, Tuan Nguyen and Cuong, Tuan Nguyen and Tsunenori, Ishioka and Masaki, Nakagawa},
 issue = {16},
 month = {Mar},
 note = {Offline Handwritten Mathematical Expression (HME) recognition has been intensively studied for two decades. However, most studies have worked on rendered images of online HMEs (Rendered Domain, or RD in short), but not on optically scanned HME images (Optical Domain, or OD in short). Due to a large gap between these two domains, it is challenging to use an HME recognizer trained in RD to recognize patterns in OD. To utilize a recognizer of RD in recognizing OD patterns, we propose a visual constrained CycleGAN (CCycleGAN) model to generate synthetic OD patterns from RD patterns and vice versa. For the RD-to-OD direction, we train a new recognizer of OD using the synthetic patterns. For the OD-to-RD direction, we utilize a pre-trained HME recognizer of RD to recognize the synthetic RD patterns generated from the OD patterns. Our experiments show that the CCycleGAN model performs significantly better than the CycleGAN model in terms of recognition accuracy. The recognition results demonstrate the effectiveness of our method in recognizing OD patterns using labeled RD patterns and unlabeled OD patterns for training. In addition, our CCycleGAN can well perform even when using only 100 training patterns in OD., Offline Handwritten Mathematical Expression (HME) recognition has been intensively studied for two decades. However, most studies have worked on rendered images of online HMEs (Rendered Domain, or RD in short), but not on optically scanned HME images (Optical Domain, or OD in short). Due to a large gap between these two domains, it is challenging to use an HME recognizer trained in RD to recognize patterns in OD. To utilize a recognizer of RD in recognizing OD patterns, we propose a visual constrained CycleGAN (CCycleGAN) model to generate synthetic OD patterns from RD patterns and vice versa. For the RD-to-OD direction, we train a new recognizer of OD using the synthetic patterns. For the OD-to-RD direction, we utilize a pre-trained HME recognizer of RD to recognize the synthetic RD patterns generated from the OD patterns. Our experiments show that the CCycleGAN model performs significantly better than the CycleGAN model in terms of recognition accuracy. The recognition results demonstrate the effectiveness of our method in recognizing OD patterns using labeled RD patterns and unlabeled OD patterns for training. In addition, our CCycleGAN can well perform even when using only 100 training patterns in OD.},
 title = {Visual Constraints for Generating Multi-Domain Offline Handwritten Mathematical Expressions},
 year = {2022}
}