This project aims to reconstruct the offline character images back to online handwritten trajectories using an encoder-decoder with attention and GMM.
For questions or more details, please contact us via email addresses: ntuanhung@gmail.com or nakagawa (at) cc.tuat.ac.jp
In this part, we provide samples as animated movies rather than static figures in the paper. Moreover, we show here more samples than in the paper.
In the following figures, the original trajectories are colored by black and the recovered trajectories are colored by red. For figures generated by the attention layer, the focused regions on original trajectories at every step are shown by brighter color.
Next, the samples are successfully recovered either with or without the attention layer.
With attention layer | Without attention layer | |
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Samples in paper |
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Other samples |
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The samples were successfully recovered with the attention layer, but unsuccessfully recovered without the attention layer.
With attention layer | Without attention layer | |
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Samples in paper |
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Other samples |
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The samples are unsuccessfully recovered either with or without the attention layer.
With attention layer | Without attention layer | |
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Samples in paper |
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Although the following samples are successfully recovered, their online trajectories consist of more points than the original ones, as shown in Figure 6 (a).
Original trajectories | Recovered trajectories | |
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Sample in paper | ![]() |
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Other samples | ![]() |
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As shown in Figure 6 (b), there are incompletely recovered samples when attention was stuck.
Original trajectories | Recovered trajectories | |
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Sample in paper | ![]() |
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Other samples | ![]() |
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