@@ -92,8 +92,6 @@ Similar to the training set, the test set also needs to be provided a folder con
If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads).
Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，download the lmdb format dataset required for benchmark
If you want to reproduce the paper SAR, you need to download extra dataset [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), extraction code: 627x. Besides, icdar2013, icdar2015, cocotext, IIIT5k datasets are also used to train. For specific details, please refer to the paper SAR.
PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
```
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@@ -194,11 +192,11 @@ First download the pretrain model, you can download the trained model to finetun
@@ -218,31 +217,13 @@ PaddleOCR supports alternating training and evaluation. You can modify `eval_bat
If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:
| Configuration file | Algorithm | backbone | trans | seq | pred |
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported at [rec_algorithm](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_overview.md):
For training Chinese data, it is recommended to use
[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_v2.0.yml` as an example:
[ch_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.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:
Take `ch_PP-OCRv3_rec.yml` as an example:
```
Global:
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@@ -276,7 +257,7 @@ Train:
...
- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
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loader:
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@@ -296,7 +277,7 @@ Eval:
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- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 32, 320]
image_shape: [3, 48, 320]
...
loader:
# Eval batch_size for Single card
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@@ -372,11 +353,11 @@ Knowledge distillation is supported in PaddleOCR for text recognition training p
## 3. Evalution
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` file.
```
# GPU evaluation, Global.checkpoints is the weight to be tested
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
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@@ -462,7 +443,7 @@ If you have a model trained on your own dataset with a different dictionary file
After the conversion is successful, there are three files in the model save directory:
```
inference/det_db/
inference/en_PP-OCRv3_rec/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
