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baidu

paddleocr

RUN ANYWHERE

Model for table extraction that receives an image as input, runs OCR on the image, and returns the text within the image and its bounding boxes.

data ingestionoptical character detectionoptical character recognitiontable extractionextractionnemo retrieverrun on rtx
API Reference
Accelerated by DGX Cloud
Deploying your application in production? Get started with a 90-day evaluation of NVIDIA AI Enterprise

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Pull and Run the NIM

$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.

export NGC_API_KEY=<PASTE_API_KEY_HERE>
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
docker run -it --rm \
    --gpus all \
    --shm-size=16GB \
    -e NGC_API_KEY=$NGC_API_KEY \
    -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
    -u $(id -u) \
    -p 8000:8000 \
    nvcr.io/nim/baidu/paddleocr:latest

Step 3
Test the NIM

You can now make a local API call using this curl command:

HOSTNAME="localhost"
SERVICE_PORT=8000
curl -X "POST" \
  "http://${HOSTNAME}:${SERVICE_PORT}/v1/infer" \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
        "input": [
          {
            "type": "image_url",
            "url": "data:image/png;base64,<BASE64_ENCODED_IMAGE>"
          },
          {
            "type": "image_url",
            "url": "data:image/png;base64,<BASE64_ENCODED_IMAGE>"
          }
        ]
      }'

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.