docling/docs/examples/rapidocr_with_custom_models.py
geoHeil 3f60a0fa78
feat: Upgrade to RapidOCR 3.x (#2088)
* feat: exploring new version

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* chore: autoformat

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* feat: enable configurable runtime for rapidocr and handle new result better;

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* chore: fix linter

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* chore: use new server model

* chore:  change default engine type to onnx

* chore: tests update for new rapidocr

* fix: rebase from main and fix clashes

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---------

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2025-08-25 12:10:33 +02:00

59 lines
1.6 KiB
Python
Vendored

import os
from modelscope import snapshot_download
from docling.datamodel.pipeline_options import PdfPipelineOptions, RapidOcrOptions
from docling.document_converter import (
ConversionResult,
DocumentConverter,
InputFormat,
PdfFormatOption,
)
def main():
# Source document to convert
source = "https://arxiv.org/pdf/2408.09869v4"
# Download RappidOCR models from HuggingFace
print("Downloading RapidOCR models")
download_path = snapshot_download(repo_id="RapidAI/RapidOCR")
# Setup RapidOcrOptions for english detection
det_model_path = os.path.join(
download_path, "onnx", "PP-OCRv5", "det", "ch_PP-OCRv5_server_det.onnx"
)
rec_model_path = os.path.join(
download_path, "onnx", "PP-OCRv5", "rec", "ch_PP-OCRv5_rec_server_infer.onnx"
)
cls_model_path = os.path.join(
download_path, "onnx", "PP-OCRv4", "cls", "ch_ppocr_mobile_v2.0_cls_infer.onnx"
)
ocr_options = RapidOcrOptions(
det_model_path=det_model_path,
rec_model_path=rec_model_path,
cls_model_path=cls_model_path,
)
pipeline_options = PdfPipelineOptions(
ocr_options=ocr_options,
)
# Convert the document
converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_options=pipeline_options,
),
},
)
conversion_result: ConversionResult = converter.convert(source=source)
doc = conversion_result.document
md = doc.export_to_markdown()
print(md)
if __name__ == "__main__":
main()