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* feat: add support for `ocrmac` OCR engine on macOS - Integrates `ocrmac` as an OCR engine option for macOS users. - Adds configuration options and dependencies for `ocrmac`. - Updates documentation to reflect new engine support. This change allows macOS users to utilize `ocrmac` for improved OCR performance and compatibility. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * updated the poetry lock Signed-off-by: Suhwan Seo <nuridol@gmail.com> * Fix linting issues, update CLI docs, and add error for ocrmac use on non-Mac systems - Resolved formatting and linting issues - Updated `--ocr-engine` CLI option documentation for `ocrmac` - Added RuntimeError for attempts to use `ocrmac` on non-Mac platforms Signed-off-by: Suhwan Seo <nuridol@gmail.com> * feat: add support for `ocrmac` OCR engine on macOS - Integrates `ocrmac` as an OCR engine option for macOS users. - Adds configuration options and dependencies for `ocrmac`. - Updates documentation to reflect new engine support. This change allows macOS users to utilize `ocrmac` for improved OCR performance and compatibility. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * docs: update examples and installation for ocrmac support - Added `OcrMacOptions` to `custom_convert.py` and `full_page_ocr.py` examples. - Included usage comments and examples for `OcrMacOptions` in OCR pipelines. - Updated installation guide to include instructions for installing `ocrmac`, noting macOS version requirements (10.15+). - Highlighted that `ocrmac` leverages Apple's Vision framework as an OCR backend. This enhances documentation for users working on macOS to leverage `ocrmac` effectively. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * fix: update `ocrmac` dependency with macOS-specific marker - Added `sys_platform == 'darwin'` marker to the `ocrmac` dependency in `pyproject.toml` to specify macOS compatibility. - Updated the content hash in `poetry.lock` to reflect the changes. This ensures the `ocrmac` dependency is only installed on macOS systems. Signed-off-by: Suhwan Seo <nuridol@gmail.com> --------- Signed-off-by: Suhwan Seo <nuridol@gmail.com> Co-authored-by: Suhwan Seo <nuridol@gmail.com>
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
import sys
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from pathlib import Path
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from typing import List
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from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import (
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EasyOcrOptions,
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OcrMacOptions,
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OcrOptions,
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PdfPipelineOptions,
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TesseractCliOcrOptions,
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TesseractOcrOptions,
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)
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from .verify_utils import verify_conversion_result_v1, verify_conversion_result_v2
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GENERATE_V1 = False
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GENERATE_V2 = False
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def get_pdf_paths():
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# Define the directory you want to search
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directory = Path("./tests/data_scanned")
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# List all PDF files in the directory and its subdirectories
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pdf_files = sorted(directory.rglob("*.pdf"))
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return pdf_files
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def get_converter(ocr_options: OcrOptions):
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pipeline_options = PdfPipelineOptions()
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pipeline_options.do_ocr = True
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pipeline_options.do_table_structure = True
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pipeline_options.table_structure_options.do_cell_matching = True
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pipeline_options.ocr_options = ocr_options
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converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(
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pipeline_options=pipeline_options,
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backend=DoclingParseDocumentBackend,
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)
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}
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)
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return converter
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def test_e2e_conversions():
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pdf_paths = get_pdf_paths()
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engines: List[OcrOptions] = [
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EasyOcrOptions(),
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TesseractOcrOptions(),
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TesseractCliOcrOptions(),
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EasyOcrOptions(force_full_page_ocr=True),
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TesseractOcrOptions(force_full_page_ocr=True),
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TesseractCliOcrOptions(force_full_page_ocr=True),
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]
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# only works on mac
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if "darwin" == sys.platform:
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engines.append(OcrMacOptions())
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engines.append(OcrMacOptions(force_full_page_ocr=True))
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for ocr_options in engines:
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print(f"Converting with ocr_engine: {ocr_options.kind}")
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converter = get_converter(ocr_options=ocr_options)
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for pdf_path in pdf_paths:
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print(f"converting {pdf_path}")
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doc_result: ConversionResult = converter.convert(pdf_path)
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verify_conversion_result_v1(
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input_path=pdf_path,
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doc_result=doc_result,
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generate=GENERATE_V1,
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fuzzy=True,
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)
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verify_conversion_result_v2(
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input_path=pdf_path,
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doc_result=doc_result,
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generate=GENERATE_V2,
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fuzzy=True,
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)
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