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feat: add support for ocrmac OCR engine on macOS (#276)
* 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>
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nuridol
GitHub
118
docling/models/ocr_mac_model.py
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118
docling/models/ocr_mac_model.py
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import logging
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import tempfile
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from typing import Iterable, Optional, Tuple
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from docling_core.types.doc import BoundingBox, CoordOrigin
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from docling.datamodel.base_models import OcrCell, Page
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import OcrMacOptions
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from docling.datamodel.settings import settings
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from docling.models.base_ocr_model import BaseOcrModel
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from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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class OcrMacModel(BaseOcrModel):
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def __init__(self, enabled: bool, options: OcrMacOptions):
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super().__init__(enabled=enabled, options=options)
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self.options: OcrMacOptions
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self.scale = 3 # multiplier for 72 dpi == 216 dpi.
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if self.enabled:
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install_errmsg = (
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"ocrmac is not correctly installed. "
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"Please install it via `pip install ocrmac` to use this OCR engine. "
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"Alternatively, Docling has support for other OCR engines. See the documentation: "
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"https://ds4sd.github.io/docling/installation/"
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)
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try:
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from ocrmac import ocrmac
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except ImportError:
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raise ImportError(install_errmsg)
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self.reader_RIL = ocrmac.OCR
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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) -> Iterable[Page]:
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if not self.enabled:
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yield from page_batch
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return
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for page in page_batch:
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assert page._backend is not None
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if not page._backend.is_valid():
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yield page
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else:
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with TimeRecorder(conv_res, "ocr"):
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ocr_rects = self.get_ocr_rects(page)
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all_ocr_cells = []
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for ocr_rect in ocr_rects:
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# Skip zero area boxes
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if ocr_rect.area() == 0:
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continue
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high_res_image = page._backend.get_page_image(
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scale=self.scale, cropbox=ocr_rect
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)
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with tempfile.NamedTemporaryFile(
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suffix=".png", mode="w"
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) as image_file:
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fname = image_file.name
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high_res_image.save(fname)
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boxes = self.reader_RIL(
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fname,
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recognition_level=self.options.recognition,
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framework=self.options.framework,
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language_preference=self.options.lang,
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).recognize()
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im_width, im_height = high_res_image.size
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cells = []
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for ix, (text, confidence, box) in enumerate(boxes):
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x = float(box[0])
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y = float(box[1])
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w = float(box[2])
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h = float(box[3])
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x1 = x * im_width
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y2 = (1 - y) * im_height
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x2 = x1 + w * im_width
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y1 = y2 - h * im_height
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left = x1 / self.scale
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top = y1 / self.scale
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right = x2 / self.scale
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bottom = y2 / self.scale
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cells.append(
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OcrCell(
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id=ix,
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text=text,
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confidence=confidence,
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bbox=BoundingBox.from_tuple(
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coord=(left, top, right, bottom),
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origin=CoordOrigin.TOPLEFT,
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),
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)
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)
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# del high_res_image
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all_ocr_cells.extend(cells)
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# Post-process the cells
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page.cells = self.post_process_cells(all_ocr_cells, page.cells)
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# DEBUG code:
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if settings.debug.visualize_ocr:
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self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects)
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yield page
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