自定义转换

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import json
import logging
import time
from pathlib import Path

import json import logging import time from pathlib import Path

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from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import (
    AcceleratorDevice,
    AcceleratorOptions,
    PdfPipelineOptions,
)
from docling.document_converter import DocumentConverter, PdfFormatOption

from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import ( AcceleratorDevice, AcceleratorOptions, PdfPipelineOptions, ) from docling.document_converter import DocumentConverter, PdfFormatOption

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_log = logging.getLogger(__name__)

_log = logging.getLogger(__name__)

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def main():
    logging.basicConfig(level=logging.INFO)

    input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")

    ###########################################################################

    # The following sections contain a combination of PipelineOptions
    # and PDF Backends for various configurations.
    # Uncomment one section at the time to see the differences in the output.

    # PyPdfium without EasyOCR
    # --------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = False
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = False

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(
    #             pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
    #         )
    #     }
    # )

    # PyPdfium with EasyOCR
    # -----------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = True
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(
    #             pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
    #         )
    #     }
    # )

    # Docling Parse without EasyOCR
    # -------------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = False
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    #     }
    # )

    # Docling Parse with EasyOCR
    # ----------------------
    pipeline_options = PdfPipelineOptions()
    pipeline_options.do_ocr = True
    pipeline_options.do_table_structure = True
    pipeline_options.table_structure_options.do_cell_matching = True
    pipeline_options.ocr_options.lang = ["es"]
    pipeline_options.accelerator_options = AcceleratorOptions(
        num_threads=4, device=AcceleratorDevice.AUTO
    )

    doc_converter = DocumentConverter(
        format_options={
            InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
        }
    )

    # Docling Parse with EasyOCR (CPU only)
    # ----------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = True
    # pipeline_options.ocr_options.use_gpu = False  # <-- set this.
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    #     }
    # )

    # Docling Parse with Tesseract
    # ----------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = True
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True
    # pipeline_options.ocr_options = TesseractOcrOptions()

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    #     }
    # )

    # Docling Parse with Tesseract CLI
    # ----------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = True
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True
    # pipeline_options.ocr_options = TesseractCliOcrOptions()

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    #     }
    # )

    # Docling Parse with ocrmac(Mac only)
    # ----------------------
    # pipeline_options = PdfPipelineOptions()
    # pipeline_options.do_ocr = True
    # pipeline_options.do_table_structure = True
    # pipeline_options.table_structure_options.do_cell_matching = True
    # pipeline_options.ocr_options = OcrMacOptions()

    # doc_converter = DocumentConverter(
    #     format_options={
    #         InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    #     }
    # )

    ###########################################################################

    start_time = time.time()
    conv_result = doc_converter.convert(input_doc_path)
    end_time = time.time() - start_time

    _log.info(f"Document converted in {end_time:.2f} seconds.")

    ## Export results
    output_dir = Path("scratch")
    output_dir.mkdir(parents=True, exist_ok=True)
    doc_filename = conv_result.input.file.stem

    # Export Deep Search document JSON format:
    with (output_dir / f"{doc_filename}.json").open("w", encoding="utf-8") as fp:
        fp.write(json.dumps(conv_result.document.export_to_dict()))

    # Export Text format:
    with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp:
        fp.write(conv_result.document.export_to_text())

    # Export Markdown format:
    with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp:
        fp.write(conv_result.document.export_to_markdown())

    # Export Document Tags format:
    with (output_dir / f"{doc_filename}.doctags").open("w", encoding="utf-8") as fp:
        fp.write(conv_result.document.export_to_document_tokens())

def main(): logging.basicConfig(level=logging.INFO) input_doc_path = Path("./tests/data/pdf/2206.01062.pdf") ########################################################################### # The following sections contain a combination of PipelineOptions # and PDF Backends for various configurations. # Uncomment one section at the time to see the differences in the output. # PyPdfium without EasyOCR # -------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = False # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = False # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption( # pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend # ) # } # ) # PyPdfium with EasyOCR # ----------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = True # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption( # pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend # ) # } # ) # Docling Parse without EasyOCR # ------------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = False # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) # } # ) # Docling Parse with EasyOCR # ---------------------- pipeline_options = PdfPipelineOptions() pipeline_options.do_ocr = True pipeline_options.do_table_structure = True pipeline_options.table_structure_options.do_cell_matching = True pipeline_options.ocr_options.lang = ["es"] pipeline_options.accelerator_options = AcceleratorOptions( num_threads=4, device=AcceleratorDevice.AUTO ) doc_converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) } ) # Docling Parse with EasyOCR (CPU only) # ---------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = True # pipeline_options.ocr_options.use_gpu = False # <-- set this. # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) # } # ) # Docling Parse with Tesseract # ---------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = True # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # pipeline_options.ocr_options = TesseractOcrOptions() # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) # } # ) # Docling Parse with Tesseract CLI # ---------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = True # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # pipeline_options.ocr_options = TesseractCliOcrOptions() # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) # } # ) # Docling Parse with ocrmac(Mac only) # ---------------------- # pipeline_options = PdfPipelineOptions() # pipeline_options.do_ocr = True # pipeline_options.do_table_structure = True # pipeline_options.table_structure_options.do_cell_matching = True # pipeline_options.ocr_options = OcrMacOptions() # doc_converter = DocumentConverter( # format_options={ # InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) # } # ) ########################################################################### start_time = time.time() conv_result = doc_converter.convert(input_doc_path) end_time = time.time() - start_time _log.info(f"Document converted in {end_time:.2f} seconds.") ## Export results output_dir = Path("scratch") output_dir.mkdir(parents=True, exist_ok=True) doc_filename = conv_result.input.file.stem # Export Deep Search document JSON format: with (output_dir / f"{doc_filename}.json").open("w", encoding="utf-8") as fp: fp.write(json.dumps(conv_result.document.export_to_dict())) # Export Text format: with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp: fp.write(conv_result.document.export_to_text()) # Export Markdown format: with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp: fp.write(conv_result.document.export_to_markdown()) # Export Document Tags format: with (output_dir / f"{doc_filename}.doctags").open("w", encoding="utf-8") as fp: fp.write(conv_result.document.export_to_document_tokens())

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if __name__ == "__main__":
    main()

if __name__ == "__main__": main()