add cppd u14m train model and doc (#11052)

* add cppd u14m train model * add cppd u14m train model and doc
PaddlePaddle · Oct 11, 2023 · 3786b27 · 3786b27
1 parent f59d092
commit 3786b27
Show file tree

Hide file tree

Showing 3 changed files with 29 additions and 8 deletions.
diff --git 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
@@ -37,22 +37,31 @@ 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-<br/>Oriented  |Artistic |Contextless| Salient  | Multi-<br/>word | General | Avg |     下载链接       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
 | CPPD Tiny  | 52.4  | 12.3 |   48.2   | 54.4  | 61.5 | 53.4 | 61.4 | 49.10 | 同上表 |
 | 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<br/>857 |  SVT  |IIIT5k<br/>3000 |IC15<br/>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-<br/>Oriented  |Artistic |Contextless| Salient  | Multi-<br/>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) |
 
-
 <a name="2"></a>
 ## 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
@@ -27,7 +27,7 @@ Scene text recognition models based on deep learning typically follow an Encoder
 <a name="model"></a>
 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<br/>857 |  SVT  |IIIT5k<br/>3000 |IC15<br/>1811| SVTP  |CUTE80 | Avg |      Download       |
@@ -36,15 +36,25 @@ 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-<br/>Oriented  |Artistic |Contextless| Salient  | Multi-<br/>word | General | Avg |     Download       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
 | CPPD Tiny  | 52.4  | 12.3 |   48.2   | 54.4  | 61.5 | 53.4 | 61.4 | 49.10 | Same as the table above. |
 | 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<br/>857 |  SVT  |IIIT5k<br/>3000 |IC15<br/>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-<br/>Oriented  |Artistic |Contextless| Salient  | Multi-<br/>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       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|