v0.5.0

What's Changed

New version specifiers

To go further forward making OnnxTR the choice for production scenarios 2 new installation options was added:

pip install "onnxtr[cpu-headless]"  # same as "onnxtr[cpu]" but with opencv-headless
pip install "onnxtr[gpu-headless]"  # same as "onnxtr[gpu]" but with opencv-headless

Disable page orientation classification

• If you deal with documents which contains only small rotations (~ -45 to 45 degrees), you can disable the page orientation classification to speed up the inference.
• This will only have an effect with assume_straight_pages=False and/or straighten_pages=True and/or detect_orientation=True.

from onnxtr.models import ocr_predictor
model = ocr_predictor(assume_straight_pages=False, disable_page_orientation=True)

Disable crop orientation classification

• If you deal with documents which contains only horizontal text, you can disable the crop orientation classification to speed up the inference.
• This will only have an effect with assume_straight_pages=False and/or straighten_pages=True.

from onnxtr.models import ocr_predictor
model = ocr_predictor(assume_straight_pages=False, disable_crop_orientation=True)

Loading custom exported orientation classification models

Syncronized with docTR:

from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor, mobilenet_v3_small_page_orientation, mobilenet_v3_small_crop_orientation
from onnxtr.models.classification.zoo import crop_orientation_predictor, page_orientation_predictor
custom_page_orientation_model = mobilenet_v3_small_page_orientation("<PATH_TO_CUSTOM_EXPORTED_ONNX_MODEL>")
custom_crop_orientation_model = mobilenet_v3_small_crop_orientation("<PATH_TO_CUSTOM_EXPORTED_ONNX_MODEL>"))

predictor = ocr_predictor(assume_straight_pages=False, detect_orientation=True)

# Overwrite the default orientation models
predictor.crop_orientation_predictor = crop_orientation_predictor(custom_crop_orientation_model)
predictor.page_orientation_predictor = page_orientation_predictor(custom_page_orientation_model)

FP16 Support

• GPU only feature (OnnxTR needs to run on GPU)
• Added a script which can be used to convert the default FP32 models to FP16 (Input / Output will be unchanged fp32), this will further speed up the inference on GPU and lower the required VRAM
• Script is available at: https://github.com/felixdittrich92/OnnxTR/blob/main/scripts/convert_to_float16.py

Full Changelog: v0.4.1...v0.5.0