@@ -158,10 +157,6 @@ We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/
...
@@ -158,10 +157,6 @@ We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
Many user hopes package the PaddleOCR service into an docker image, so that it can be quickly released and used in the docker or k8s environment.
This page provide some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue)
## 1. Prerequisites
You need to install the following basic components first：
a. Docker
b. Graphics driver and CUDA 10.0+（GPU）
c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
b. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
```
cd docker/cpu
```
c. Build image
```
docker build -t paddleocr:cpu .
```
## 3. Start container
a. CPU version
```
sudo docker run -dp 8866:8866 --name paddle_ocr paddleocr:cpu
```
b. GPU version (base on NVIDIA Container Toolkit)
```
sudo nvidia-docker run -dp 8866:8866 --name paddle_ocr paddleocr:gpu
```
c. GPU version (Docker 19.03++)
```
sudo docker run -dp 8866:8866 --gpus all --name paddle_ocr paddleocr:gpu
```
d. Check service status（If you can see the following statement then it means completed：Successfully installed ocr_system && Running on http://0.0.0.0:8866/）
```
docker logs -f paddle_ocr
```
## 4. Test
a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/）
b. Post a service request（sample request in sample_request.txt）
```
curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8866/predict/ocr_system
```
c. Get resposne（If the call is successful, the following result will be returned）
