Merge pull request #6090 from Intsigstephon/add_rosetta_rare_doc
add rosetta rare ch docpull/6080/head
MissPenguin
GitHub
commit
ac8d91cb91
|
|
@ -0,0 +1,121 @@
|
|||
# RARE
|
||||
|
||||
- [1. 算法简介](#1)
|
||||
- [2. 环境配置](#2)
|
||||
- [3. 模型训练、评估、预测](#3)
|
||||
- [3.1 训练](#3-1)
|
||||
- [3.2 评估](#3-2)
|
||||
- [3.3 预测](#3-3)
|
||||
- [4. 推理部署](#4)
|
||||
- [4.1 Python推理](#4-1)
|
||||
- [4.2 C++推理](#4-2)
|
||||
- [4.3 Serving服务化部署](#4-3)
|
||||
- [4.4 更多推理部署](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. 算法简介
|
||||
|
||||
论文信息：
|
||||
> [Robust Scene Text Recognition with Automatic Rectification](https://arxiv.org/abs/1603.03915v2)
|
||||
> Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai∗
|
||||
> CVPR, 2016
|
||||
|
||||
使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
|
||||
|
||||
|模型|骨干网络|配置文件|Avg Accuracy|下载链接|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.6%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|
||||
|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.5%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_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。
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. 模型训练、评估、预测
|
||||
|
||||
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。以基于Resnet34_vd骨干网络为例:
|
||||
|
||||
<a name="3-1"></a>
|
||||
### 3.1 训练
|
||||
|
||||
```
|
||||
#单卡训练（训练周期长，不建议）
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
|
||||
#多卡训练，通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
|
||||
```
|
||||
|
||||
<a name="3-2"></a>
|
||||
### 3.2 评估
|
||||
|
||||
```
|
||||
# GPU评估, Global.pretrained_model为待评估模型
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
<a name="3-3"></a>
|
||||
### 3.3 预测
|
||||
|
||||
```
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. 推理部署
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python推理
|
||||
首先将RARE文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，在MJSynth和SynthText两个文字识别数据集训练得到的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar) )，可以使用如下命令进行转换：
|
||||
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rare
|
||||
```
|
||||
|
||||
RARE文本识别模型推理，可以执行如下命令：
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rare/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
推理结果如下所示：
|
||||
|
||||
|
||||
|
||||
```
|
||||
Predicts of doc/imgs_words/en/word_1.png:('joint ', 0.9999969601631165)
|
||||
```
|
||||
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++推理
|
||||
|
||||
暂不支持
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving服务化部署
|
||||
|
||||
暂不支持
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 更多推理部署
|
||||
|
||||
RARE模型还支持以下推理部署方式：
|
||||
|
||||
- Paddle2ONNX推理：准备好推理模型后，参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```bibtex
|
||||
@inproceedings{2016Robust,
|
||||
title={Robust Scene Text Recognition with Automatic Rectification},
|
||||
author={ Shi, B. and Wang, X. and Lyu, P. and Cong, Y. and Xiang, B. },
|
||||
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year={2016},
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
# Rosetta
|
||||
|
||||
- [1. 算法简介](#1)
|
||||
- [2. 环境配置](#2)
|
||||
- [3. 模型训练、评估、预测](#3)
|
||||
- [3.1 训练](#3-1)
|
||||
- [3.2 评估](#3-2)
|
||||
- [3.3 预测](#3-3)
|
||||
- [4. 推理部署](#4)
|
||||
- [4.1 Python推理](#4-1)
|
||||
- [4.2 C++推理](#4-2)
|
||||
- [4.3 Serving服务化部署](#4-3)
|
||||
- [4.4 更多推理部署](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. 算法简介
|
||||
|
||||
论文信息：
|
||||
> [Rosetta: Large Scale System for Text Detection and Recognition in Images](https://arxiv.org/abs/1910.05085)
|
||||
> Borisyuk F , Gordo A , V Sivakumar
|
||||
> KDD, 2018
|
||||
|
||||
使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估， 算法复现效果如下：
|
||||
|
||||
|模型|骨干网络|配置文件|Avg Accuracy|下载链接|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|Rosetta|Resnet34_vd|[configs/rec/rec_r34_vd_none_none_ctc.yml](../../configs/rec/rec_r34_vd_none_none_ctc.yml)|79.11%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
|
||||
|Rosetta|MobileNetV3|[configs/rec/rec_mv3_none_none_ctc.yml](../../configs/rec/rec_mv3_none_none_ctc.yml)|75.80%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_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。
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. 模型训练、评估、预测
|
||||
|
||||
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。 以基于Resnet34_vd骨干网络为例:
|
||||
|
||||
<a name="3-1"></a>
|
||||
### 3.1 训练
|
||||
|
||||
```
|
||||
#单卡训练（训练周期长，不建议）
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
|
||||
#多卡训练，通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
|
||||
```
|
||||
|
||||
<a name="3-2"></a>
|
||||
### 3.2 评估
|
||||
|
||||
```
|
||||
# GPU评估, Global.pretrained_model为待评估模型
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
<a name="3-3"></a>
|
||||
### 3.3 预测
|
||||
|
||||
```
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. 推理部署
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python推理
|
||||
首先将Rosetta文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，在MJSynth和SynthText两个文字识别数据集训练得到的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar) )，可以使用如下命令进行转换：
|
||||
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_none_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rosetta
|
||||
```
|
||||
|
||||
Rosetta文本识别模型推理，可以执行如下命令：
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rosetta/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
|
||||
推理结果如下所示：
|
||||
|
||||
|
||||
|
||||
```
|
||||
Predicts of doc/imgs_words/en/word_1.png:('joint', 0.9999982714653015)
|
||||
```
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++推理
|
||||
|
||||
暂不支持
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving服务化部署
|
||||
|
||||
暂不支持
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 更多推理部署
|
||||
|
||||
Rosetta模型还支持以下推理部署方式：
|
||||
|
||||
- Paddle2ONNX推理：准备好推理模型后，参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```bibtex
|
||||
@inproceedings{2018Rosetta,
|
||||
title={Rosetta: Large Scale System for Text Detection and Recognition in Images},
|
||||
author={ Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath },
|
||||
booktitle={the 24th ACM SIGKDD International Conference},
|
||||
year={2018},
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
# RARE
|
||||
|
||||
- [1. Introduction](#1)
|
||||
- [2. Environment](#2)
|
||||
- [3. Model Training / Evaluation / Prediction](#3)
|
||||
- [3.1 Training](#3-1)
|
||||
- [3.2 Evaluation](#3-2)
|
||||
- [3.3 Prediction](#3-3)
|
||||
- [4. Inference and Deployment](#4)
|
||||
- [4.1 Python Inference](#4-1)
|
||||
- [4.2 C++ Inference](#4-2)
|
||||
- [4.3 Serving](#4-3)
|
||||
- [4.4 More](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. Introduction
|
||||
|
||||
Paper information:
|
||||
> [Robust Scene Text Recognition with Automatic Rectification](https://arxiv.org/abs/1603.03915v2)
|
||||
> Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai∗
|
||||
> CVPR, 2016
|
||||
|
||||
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|
||||
|
||||
|Models|Backbone Networks|Configuration Files|Avg Accuracy|Download Links|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.6%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|
||||
|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.5%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. Environment
|
||||
Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. Model Training / Evaluation / Prediction
|
||||
|
||||
Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
|
||||
|
||||
<a name="3-1"></a>
|
||||
### 3.1 Training
|
||||
|
||||
````
|
||||
#Single card training (long training period, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
|
||||
#Multi-card training, specify the card number through the --gpus parameter
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
|
||||
````
|
||||
|
||||
<a name="3-2"></a>
|
||||
### 3.2 Evaluation
|
||||
|
||||
````
|
||||
# GPU evaluation, Global.pretrained_model is the model to be evaluated
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
````
|
||||
|
||||
<a name="3-3"></a>
|
||||
### 3.3 Prediction
|
||||
|
||||
````
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
````
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. Inference
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python Inference
|
||||
First, convert the model saved during the RARE text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ([Model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar) ), which can be converted using the following command:
|
||||
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rare
|
||||
````
|
||||
|
||||
RARE text recognition model inference, you can execute the following commands:
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rare/" --rec_image_shape="3, 32, 100" --rec_char_dict_path= "./ppocr/utils/ic15_dict.txt"
|
||||
````
|
||||
The inference results are as follows:
|
||||
|
||||
|
||||
|
||||
````
|
||||
Predicts of doc/imgs_words/en/word_1.png:('joint ', 0.9999969601631165)
|
||||
````
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++ Inference
|
||||
|
||||
Not currently supported
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving
|
||||
|
||||
Not currently supported
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 More
|
||||
|
||||
The RARE model also supports the following inference deployment methods:
|
||||
|
||||
- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
## Quote
|
||||
|
||||
````bibtex
|
||||
@inproceedings{2016Robust,
|
||||
title={Robust Scene Text Recognition with Automatic Rectification},
|
||||
author={ Shi, B. and Wang, X. and Lyu, P. and Cong, Y. and Xiang, B. },
|
||||
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year={2016},
|
||||
}
|
||||
````
|
||||
|
|
@ -0,0 +1,121 @@
|
|||
# Rosetta
|
||||
|
||||
- [1. Introduction](#1)
|
||||
- [2. Environment](#2)
|
||||
- [3. Model Training / Evaluation / Prediction](#3)
|
||||
- [3.1 Training](#3-1)
|
||||
- [3.2 Evaluation](#3-2)
|
||||
- [3.3 Prediction](#3-3)
|
||||
- [4. Inference and Deployment](#4)
|
||||
- [4.1 Python Inference](#4-1)
|
||||
- [4.2 C++ Inference](#4-2)
|
||||
- [4.3 Serving](#4-3)
|
||||
- [4.4 More](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. Introduction
|
||||
|
||||
Paper information:
|
||||
> [Rosetta: Large Scale System for Text Detection and Recognition in Images](https://arxiv.org/abs/1910.05085)
|
||||
> Borisyuk F , Gordo A , V Sivakumar
|
||||
> KDD, 2018
|
||||
|
||||
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|
||||
|
||||
|Models|Backbone Networks|Configuration Files|Avg Accuracy|Download Links|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|Rosetta|Resnet34_vd|[configs/rec/rec_r34_vd_none_none_ctc.yml](../../configs/rec/rec_r34_vd_none_none_ctc.yml)|79.11%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
|
||||
|Rosetta|MobileNetV3|[configs/rec/rec_mv3_none_none_ctc.yml](../../configs/rec/rec_mv3_none_none_ctc.yml)|75.80%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. Environment
|
||||
Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. Model Training / Evaluation / Prediction
|
||||
|
||||
Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
|
||||
|
||||
<a name="3-1"></a>
|
||||
### 3.1 Training
|
||||
|
||||
````
|
||||
#Single card training (long training period, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
|
||||
#Multi-card training, specify the card number through the --gpus parameter
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
|
||||
````
|
||||
|
||||
<a name="3-2"></a>
|
||||
### 3.2 Evaluation
|
||||
|
||||
````
|
||||
# GPU evaluation, Global.pretrained_model is the model to be evaluated
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
````
|
||||
|
||||
<a name="3-3"></a>
|
||||
### 3.3 Prediction
|
||||
|
||||
````
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
````
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. Inference and Deployment
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python Inference
|
||||
First, convert the model saved during the Rosetta text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ( [Model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar) ), which can be converted using the following command:
|
||||
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_none_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rosetta
|
||||
````
|
||||
|
||||
Rosetta text recognition model inference, you can execute the following commands:
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rosetta/" --rec_image_shape="3, 32, 100" --rec_char_dict_path= "./ppocr/utils/ic15_dict.txt"
|
||||
````
|
||||
|
||||
The inference results are as follows:
|
||||
|
||||
|
||||
|
||||
````
|
||||
Predicts of doc/imgs_words/en/word_1.png:('joint', 0.9999982714653015)
|
||||
````
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++ Inference
|
||||
|
||||
Not currently supported
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving
|
||||
|
||||
Not currently supported
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 More
|
||||
|
||||
The Rosetta model also supports the following inference deployment methods:
|
||||
|
||||
- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
## Quote
|
||||
|
||||
````bibtex
|
||||
@inproceedings{2018Rosetta,
|
||||
title={Rosetta: Large Scale System for Text Detection and Recognition in Images},
|
||||
author={ Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath },
|
||||
booktitle={the 24th ACM SIGKDD International Conference},
|
||||
year={2018},
|
||||
}
|
||||
````
|
||||
Loading…
Reference in New Issue