提交 25820aae 编写于 作者: T Topdu

[doc] add svtr cite link

上级 f3c0bc8f
...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
## 1. 算法简介 ## 1. 算法简介
论文信息: 论文信息:
> [SVTR: Scene Text Recognition with a Single Visual Model]() > [SVTR: Scene Text Recognition with a Single Visual Model](https://arxiv.org/abs/2205.00159)
> Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang > Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang
> IJCAI, 2022 > IJCAI, 2022
...@@ -159,3 +159,16 @@ Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999998807907104) ...@@ -159,3 +159,16 @@ Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999998807907104)
## 5. FAQ ## 5. FAQ
1. 由于`SVTR`使用的算子大多为矩阵相乘,在GPU环境下,速度具有优势,但在CPU开启mkldnn加速环境下,`SVTR`相比于被优化的卷积网络没有优势。 1. 由于`SVTR`使用的算子大多为矩阵相乘,在GPU环境下,速度具有优势,但在CPU开启mkldnn加速环境下,`SVTR`相比于被优化的卷积网络没有优势。
## 引用
```bibtex
@article{Du2022SVTR,
author = {Du, Yongkun and Chen, Zhineng and Jia, Caiyan and Yin, Xiaoting and Zheng, Tianlun and Li, Chenxia and Du, Yuning and Jiang, Yu-Gang},
title = {SVTR: Scene Text Recognition with a Single Visual Model},
publisher = {IJCAI},
year = {2022},
url = {https://arxiv.org/abs/2205.00159}
}
```
...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
## 1. Introduction ## 1. Introduction
Paper: Paper:
> [SVTR: Scene Text Recognition with a Single Visual Model]() > [SVTR: Scene Text Recognition with a Single Visual Model](https://arxiv.org/abs/2205.00159)
> Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang > Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang
> IJCAI, 2022 > IJCAI, 2022
...@@ -131,3 +131,15 @@ Not supported ...@@ -131,3 +131,15 @@ Not supported
## 5. FAQ ## 5. FAQ
1. Since most of the op operators used by `SVTR` are matrix multiplication, in the GPU environment, the speed has an advantage, but in the environment where mkldnn is enabled on the CPU, `SVTR` has no advantage over the optimized convolutional network. 1. Since most of the op operators used by `SVTR` are matrix multiplication, in the GPU environment, the speed has an advantage, but in the environment where mkldnn is enabled on the CPU, `SVTR` has no advantage over the optimized convolutional network.
## Citation
```bibtex
@article{Du2022SVTR,
author = {Du, Yongkun and Chen, Zhineng and Jia, Caiyan and Yin, Xiaoting and Zheng, Tianlun and Li, Chenxia and Du, Yuning and Jiang, Yu-Gang},
title = {SVTR: Scene Text Recognition with a Single Visual Model},
publisher = {IJCAI},
year = {2022},
url = {https://arxiv.org/abs/2205.00159}
}
```
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