diff --git a/configs/rec/rec_svtrnet_cppd_base_en.yml b/configs/rec/rec_svtrnet_cppd_base_en.yml
index cca1151c42..99885fb066 100644
--- a/configs/rec/rec_svtrnet_cppd_base_en.yml
+++ b/configs/rec/rec_svtrnet_cppd_base_en.yml
@@ -92,7 +92,7 @@ Train:
Eval:
dataset:
- name: LMDBDataset
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
diff --git a/doc/doc_ch/algorithm_rec_cppd.md b/doc/doc_ch/algorithm_rec_cppd.md
index 0fd2fd04e2..1d48ed3059 100644
--- a/doc/doc_ch/algorithm_rec_cppd.md
+++ b/doc/doc_ch/algorithm_rec_cppd.md
@@ -37,7 +37,7 @@ CPPD在场景文本识别公开数据集上的精度(%)和模型文件如下:
| CPPD Base | 98.2 | 95.5 | 97.6 | 87.9 | 90.0 | 92.7 | 93.80 | [英文](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar)|
| CPPD Base 48*160 | 97.5 | 95.5 | 97.7 | 87.7 | 92.4 | 93.7 | 94.10 | [英文](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_48_160_en_train.tar) |
-* 在英文合成数据集(MJ+ST)训练,在英文Union14M-L benchmark测试结果[U14m](https://github.com/Mountchicken/Union14M/)。
+* 英文合成数据集(MJ+ST)训练,英文Union14M-L benchmark测试结果[U14m](https://github.com/Mountchicken/Union14M/)。
| 模型 |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | 下载链接 |
|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
@@ -45,14 +45,23 @@ CPPD在场景文本识别公开数据集上的精度(%)和模型文件如下:
| CPPD Base | 65.5 | 18.6 | 56.0 | 61.9 | 71.0 | 57.5 | 65.8 | 56.63 | 同上表 |
| CPPD Base 48*160 | 71.9 | 22.1 | 60.5 | 67.9 | 78.3 | 63.9 | 67.1 | 61.69 | 同上表 |
-* 中文训练集和测试集来自于[Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) 。
+* Union14M-L 训练集训练,英文测试结果。
+
+| 模型 |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg | 下载链接 |
+|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|
+| CPPD Base 32*128 | 98.7 | 98.5 | 99.4 | 91.7 | 96.7 | 99.7 | 97.44 | [英文](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_u14m_train.tar) |
+
+| 模型 |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | 下载链接 |
+|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
+| CPPD Base 32*128 | 87.5 | 70.7 | 78.2 | 82.9 | 85.5 | 85.4 | 84.3 | 82.08 | 同上表 |
+
+* 中文训练集和测试集来自于[Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition)。
| 模型 | Scene | Web | Document | Handwriting | Avg | 下载链接 |
|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|
| CPPD Base | 74.4 | 76.1 | 98.6 | 55.3 | 76.10 | [中文](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_ch_train.tar) |
| CPPD Base + STN | 78.4 | 79.3 | 98.9 | 57.6 | 78.55 | [中文](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_stn_ch_train.tar) |
-
## 2. 环境配置
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
@@ -67,7 +76,9 @@ CPPD在场景文本识别公开数据集上的精度(%)和模型文件如下:
#### 数据集准备
[英文数据集下载](https://github.com/baudm/parseq)
-[Union14M-Benchmark 下载](https://github.com/Mountchicken/Union14M)
+
+[Union14M-L 下载](https://github.com/Mountchicken/Union14M)
+
[中文数据集下载](https://github.com/fudanvi/benchmarking-chinese-text-recognition#download)
#### 启动训练
diff --git a/doc/doc_en/algorithm_rec_cppd_en.md b/doc/doc_en/algorithm_rec_cppd_en.md
index 41315ce2fe..3bc3861988 100644
--- a/doc/doc_en/algorithm_rec_cppd_en.md
+++ b/doc/doc_en/algorithm_rec_cppd_en.md
@@ -27,7 +27,7 @@ Scene text recognition models based on deep learning typically follow an Encoder
The accuracy (%) and model files of CPPD on the public dataset of scene text recognition are as follows::
-* English dataset from [PARSeq](https://github.com/baudm/parseq)。
+* English dataset from [PARSeq](https://github.com/baudm/parseq).
| Model |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg | Download |
@@ -36,7 +36,7 @@ The accuracy (%) and model files of CPPD on the public dataset of scene text rec
| CPPD Base | 98.2 | 95.5 | 97.6 | 87.9 | 90.0 | 92.7 | 93.80 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar)|
| CPPD Base 48*160 | 97.5 | 95.5 | 97.7 | 87.7 | 92.4 | 93.7 | 94.10 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_48_160_en_train.tar) |
-* Union14M-L benchmark from [U14m](https://github.com/Mountchicken/Union14M/)。
+* Trained on Synth dataset(MJ+ST), Test on Union14M-L benchmark from [U14m](https://github.com/Mountchicken/Union14M/).
| Model |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | Download |
|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
@@ -44,7 +44,17 @@ The accuracy (%) and model files of CPPD on the public dataset of scene text rec
| CPPD Base | 65.5 | 18.6 | 56.0 | 61.9 | 71.0 | 57.5 | 65.8 | 56.63 | Same as the table above. |
| CPPD Base 48*160 | 71.9 | 22.1 | 60.5 | 67.9 | 78.3 | 63.9 | 67.1 | 61.69 | Same as the table above. |
-* Chinese dataset from [Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) 。
+* Trained on Union14M-L training dataset.
+
+| Model |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg | Download |
+|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|
+| CPPD Base 32*128 | 98.7 | 98.5 | 99.4 | 91.7 | 96.7 | 99.7 | 97.44 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_u14m_train.tar) |
+
+| Model |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | Download |
+|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
+| CPPD Base 32*128 | 87.5 | 70.7 | 78.2 | 82.9 | 85.5 | 85.4 | 84.3 | 82.08 | Same as the table above. |
+
+* Chinese dataset from [Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition).
| Model | Scene | Web | Document | Handwriting | Avg | Download |
|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|