CVPR2019ocr.zip
Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into i
文件列表
CVPR2019-ocr.zip
(预估有个8文件)
1811.00357_Latent Variable Model for Multi-modal Translation.pdf
613KB
Xie_Aggregation_Cross-Entropy_for_Sequence_Recognition_CVPR_2019_paper.pdf
2.72MB
1908.03265v1_ON THE VARIANCE OF THE ADAPTIVE LEARNING.pdf
1.44MB
jtjgwld.xps
182.5MB
Bhunia_Handwriting_Recognition_in_Low-Resource_Scripts_Using_Adversarial_Learning_CVPR_2019_paper.pdf
972KB
1610.02616.pdf
1.68MB
jtjgwld.xps.pdf
162.2MB
Wang+et+al.+-+2018+-+Explore+Uncertainty+in+Residual+Networks+for+Crowds+Flow+Prediction.pdf
1.78MB
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