Comments (2)
Our work is also based on https://arxiv.org/abs/2105.07983.
Experimental results showed that CTC Loss helps the performance of a commercial OCR in the character recognition task.
We noticed that such loss function introduces small artifacts in images in a few samples. This is reflected slightly on some pixel-level metrics (PSNR mainly). However, what we think is that such loss drives the model not to produce visually excellent images but rather better images for ocr, improving its recognition accuracy (slightly).
For completeness, we leave the full table that we did not include in the paper.
Thank you for your comment.
PSNR | SSIM | LPIPS | DISTS | CER | |
---|---|---|---|---|---|
NAF-DPM w/o finetuning | 34,377 | 0.9944 | 0.004668 | 0.022891 | 1.55 |
NAF-DPM w finetuning | 34,066 | 0.9956 | 0.003934 | 0.013801 | 1.40 |
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Thank you very much for your answer!
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