diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index 6c5efc0674f23804c63dc4e10ed9fffa722b85a6..769374aedb12b6a2cfd2aa5aca8515c7bf7520a1 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -142,7 +142,7 @@ word_dict.txt 每行有一个单字,将字符与数字索引映射在一起,
- 添加空格类别
-如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `true`。
+如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `True`。
@@ -193,8 +193,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| 配置文件 | 算法名称 | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
-| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
-| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
+| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
@@ -208,9 +208,9 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
-训练中文数据,推荐使用[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
+训练中文数据,推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
-以 `rec_chinese_lite_train_v1.1.yml` 为例:
+以 `rec_chinese_lite_train_v2.0.yml` 为例:
```
Global:
...
@@ -220,7 +220,7 @@ Global:
character_type: ch
...
# 识别空格
- use_space_char: False
+ use_space_char: True
Optimizer:
@@ -300,7 +300,7 @@ Global:
character_dict_path: ./ppocr/utils/dict/french_dict.txt
...
# 识别空格
- use_space_char: False
+ use_space_char: True
...
@@ -362,12 +362,12 @@ infer_img: doc/imgs_words/en/word_1.png
word : joint
```
-预测使用的配置文件必须与训练一致,如您通过 `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml` 完成了中文模型的训练,
+预测使用的配置文件必须与训练一致,如您通过 `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml` 完成了中文模型的训练,
您可以使用如下命令进行中文模型预测。
```
# 预测中文结果
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
预测图片:
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index daa12820fd83a1dd57960502c0b0f2f860c153d4..da5a7c4731df2d4cb686f2ae0a94f9c834b3a06d 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -135,7 +135,7 @@ If you need to customize dic file, please add character_dict_path field in confi
- Add space category
-If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `true`.
+If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
**Note: use_space_char only takes effect when character_type=ch**
@@ -183,8 +183,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| Configuration file | Algorithm | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
-| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
-| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
+| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
@@ -198,9 +198,9 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
For training Chinese data, it is recommended to use
-[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
+[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
co
-Take `rec_chinese_lite_train_v1.1.yml` as an example:
+Take `rec_chinese_lite_train_v2.0.yml` as an example:
```
Global:
...
@@ -210,7 +210,7 @@ Global:
character_type: ch
...
# Whether to recognize spaces
- use_space_char: False
+ use_space_char: True
Optimizer:
@@ -290,7 +290,7 @@ Global:
character_dict_path: ./ppocr/utils/dict/french_dict.txt
...
# Whether to recognize spaces
- use_space_char: False
+ use_space_char: True
...
@@ -337,7 +337,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci
```
# Predict English results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
```
Input image:
@@ -352,11 +352,11 @@ infer_img: doc/imgs_words/en/word_1.png
word : joint
```
-The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml`, you can use the following command to predict the Chinese model:
+The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml`, you can use the following command to predict the Chinese model:
```
# Predict Chinese results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
```
Input image: