Support for Converting PaddlePaddleOCR (CPU-based) to OpenVINO for Acceleration

[mkrzywda]

I’m currently using PaddlePaddleOCR with a CPU-based setup and would like to accelerate its performance using OpenVINO. My current implementation relies on the following configuration:

ocr = PaddleOCR(
    use_mkldnn=True,
    cpu_threads=16,
    use_angle_cls=False,
    lang="en",
    ocr_version="PP-OCR",
    use_gpu=False,
    use_space_char=True,
    det_db_thresh=0.3,
    det_db_box_thresh=0.5,
    cls=False,
    bin=False,
    inv=False,
    alpha_color=False,
    max_text_length=100
)

I’ve explored the possibility of converting the model from PaddlePaddle to ONNX and then from ONNX to OpenVINO, but this approach seems overly complex and potentially inefficient. I’d like to know if there’s a more straightforward or optimized way to convert and run my PaddlePaddleOCR model with OpenVINO acceleration on CPU.

What I Need:

• Guidance or a tool to directly convert my PaddlePaddleOCR model to OpenVINO format.
• Steps to integrate and run the converted model with OpenVINO for CPU acceleration.
• Any best practices or recommendations to optimize performance in this setup.

My system:

pyclipper==1.3.0.post5
attrdict==2.0.1
albucore==0.0.16
albumentations==1.4.14
paddlepaddle==3.0.0b2
paddleocr==2.9.0
grpcio==1.68.0
grpcio-tools==1.68.0
prometheus_client==0.21.0
ruff==0.9.5
openvino
numpy
opencv-python
onnxruntime
paddle2onnx
onnxslim

[mkrzywda]

Alternatively, if it can be seamlessly converted to OpenVino from PaddlePaddle, that would be an added advantage.