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Paddle OCR

This example performs end-to-end text detection and recognition using the PaddleOCR pipeline accelerated by a Hailo-8, Hailo-8L, or Hailo-10H device. It processes images, videos, folders, or camera input, detects text regions, and decodes the text using an OCR model.

Optionally, FPS performance can be shown, and output can be saved for each input.

output example

Requirements

  • hailo_platform:
    • 4.23.0 (for Hailo-8 devices)
    • 5.3.0 (for Hailo-10H devices)
  • opencv-python
  • paddlepaddle
  • shapely
  • pyclipper
  • symspellpy

Linux Installation

Run this app in one of two ways:

  1. Standalone installation in a clean virtual environment (no TAPPAS required) — see Option 1
  2. From an installed hailo-apps repository — see Option 2

Option 1: Standalone Installation

To avoid compatibility issues, it's recommended to use a clean virtual environment.

  1. Install PCIe driver and PyHailoRT

    • Download and install the PCIe driver and PyHailoRT from the Hailo website
    • To install the PyHailoRT whl:
    pip install hailort-X.X.X-cpXX-cpXX-linux_x86_64.whl

  2. Clone the repository:

    git clone https://github.com/hailo-ai/hailo-apps.git
cd hailo-apps/python/standalone_apps/paddle_ocr

  3. Install dependencies:

    pip install -r requirements.txt

Option 2: Inside an Installed hailo-apps Repository

If you installed the full repository:

git clone https://github.com/hailo-ai/hailo-apps.git
cd hailo-apps
sudo ./install.sh
source setup_env.sh

Then the app is already ready for usage:

cd hailo-apps/python/standalone_apps/paddle_ocr

Windows Installation

To avoid compatibility issues, it's recommended to use a clean virtual environment.

  1. Install HailoRT (MSI) + PyHailoRT

    1. Download and install the HailoRT Windows MSI from the Hailo website.

    2. During the installation, make sure PyHailoRT is selected (in the MSI “Custom Setup” tree).

    3. After installation, the PyHailoRT wheel is located under: C:\Program Files\HailoRT\python

    4. Create and activate a virtual environment:

    python -m venv wind_venv
.\wind_venv\Scripts\Activate.ps1
    1. Install the PyHailoRT wheel from the MSI installation folder:
    pip install "C:\Program Files\HailoRT\python\hailort-*.whl"

  2. Clone the repository:

    git clone https://github.com/hailo-ai/hailo-apps.git
cd hailo-apps\hailo_apps\python\standalone_apps\paddle_ocr

  3. Install dependencies:

    pip install -r requirements.txt

Run

After completing either installation option, run from the application folder:

python .\paddle_ocr.py -n <det_hef> <ocr_hef> -i <input_path>

The output results will be saved under a folder named output, or in the directory specified by --output-dir.

Arguments

  • --hef-path, -n:
    • A model name (e.g., ocr_rec) → the script will automatically download and resolve the correct HEF for your device.
    • A file path to a local HEF → the script will use the specified network directly.
  • -i, --input:
    • An input source such as an image (bus.jpg), a video (video.mp4), a directory of images, or usb to auto-select the first available USB camera.
      • On Linux, you can also use /dev/vidoeX (e.g., /dev/video0) to select a specific camera.
      • On Windows, you can also use a camera index (0, 1, 2, ...) to select a specific camera.
      • On Raspberry Pi, you can also use rpi to enable the Raspberry Pi camera.
    • A predefined input name from resources_config.yaml (e.g., bus, street).
      • If you choose a predefined name, the input will be automatically downloaded if it doesn't already exist.
      • Use --list-inputs to display all available predefined inputs.
  • -b, --batch-size: [optional] Number of images in one batch. Defaults to 1.
  • -s, --save-output: [optional] Save the output of the inference from a stream.
  • -o, --output-dir: [optional] Directory where output images/videos will be saved.
  • --show-fps: [optional] Display FPS performance metrics for video/camera input.
  • --no-display: [optional] Run without opening a display window. Useful for headless or performance testing.
  • --video-unpaced: [optional] Process video input as fast as possible without respecting the original video FPS (no pacing).
  • -t, --time-to-run: [optional] Maximum runtime in seconds. Stops the application after the specified duration.
  • --use-corrector: [optional] Enable text correction after OCR (e.g., spelling or formatting fixes).
  • cr, --camera-resolution: [optional][Camera only] Input resolution: sd (640x480), hd (1280x720), or fhd (1920x1080).
  • or, --output-resolution: [optional] Set output size using sd|hd|fhd, or pass custom width/height (e.g., --output-resolution 1920 1080).
  • -f, --frame-rate: [optional][Camera only] Override the camera input framerate.
  • --list-models: [optional] Print all supported models for this application (from resources_config.yaml) and exit.
  • --list-inputs: [optional] Print the available predefined input resources (images/videos) defined in resources_config.yaml for this application, then exit.

For more information:

./paddle_ocr.py -h

Example

List supported networks

./paddle_ocr.py --list-nets

List available input resources

./paddle_ocr.py --list-inputs

Inference on single image

./paddle_ocr.py -n ocr_det.hef ocr_rec.hef -i ocr_img1.jpg

Inference on a usb camera stream

./paddle_ocr.py -n ocr_det.hef ocr_rec.hef -i camera

Inference on a usb camera stream with custom frame rate

./paddle_ocr.py -n ocr_det.hef ocr_rec.hef -i usb -f 20

Additional Notes

  • Images are only supported in the following formats: .jpg, .jpeg, .png or .bmp
  • Number of input images should be divisible by batch_size
  • For any issues, open a post on the Hailo Community

Disclaimer

This code example is provided by Hailo solely on an “AS IS” basis and “with all faults”. No responsibility or liability is accepted or shall be imposed upon Hailo regarding the accuracy, merchantability, completeness or suitability of the code example. Hailo shall not have any liability or responsibility for errors or omissions in, or any business decisions made by you in reliance on this code example or any part of it. If an error occurs when running this example, please open a ticket in the "Issues" tab.

This example was tested on specific versions and we can only guarantee the expected results using the exact version mentioned above on the exact environment. The example might work for other versions, other environment or other HEF file, but there is no guarantee that it will.