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>
This commit is contained in:
NuRi 2024-11-08 09:08:55 +09:00 committed by Suhwan Seo
parent 6c22cba0a7
commit 4aaf128384
6 changed files with 147 additions and 1 deletions

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@ -28,6 +28,7 @@ from docling.datamodel.pipeline_options import (
TableFormerMode,
TesseractCliOcrOptions,
TesseractOcrOptions,
OcrMacOptions,
)
from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption
@ -73,6 +74,7 @@ class OcrEngine(str, Enum):
EASYOCR = "easyocr"
TESSERACT_CLI = "tesseract_cli"
TESSERACT = "tesseract"
OCRMAC = "ocrmac"
def export_documents(
@ -224,6 +226,8 @@ def convert(
ocr_options = TesseractCliOcrOptions()
case OcrEngine.TESSERACT:
ocr_options = TesseractOcrOptions()
case OcrEngine.OCRMAC:
ocr_options = OcrMacOptions()
case _:
raise RuntimeError(f"Unexpected OCR engine type {ocr_engine}")

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@ -60,6 +60,16 @@ class TesseractOcrOptions(OcrOptions):
extra="forbid",
)
class OcrMacOptions(OcrOptions):
kind: Literal["ocrmac"] = "ocrmac"
lang: List[str] = ["fr-FR", "de-DE", "es-ES", "en-US"]
recognition: str = "accurate"
framework: str = "vision"
model_config = ConfigDict(
extra="forbid",
)
class PipelineOptions(BaseModel):
create_legacy_output: bool = (
@ -73,7 +83,7 @@ class PdfPipelineOptions(PipelineOptions):
do_ocr: bool = True # True: perform OCR, replace programmatic PDF text
table_structure_options: TableStructureOptions = TableStructureOptions()
ocr_options: Union[EasyOcrOptions, TesseractCliOcrOptions, TesseractOcrOptions] = (
ocr_options: Union[EasyOcrOptions, TesseractCliOcrOptions, TesseractOcrOptions, OcrMacOptions] = (
Field(EasyOcrOptions(), discriminator="kind")
)

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@ -0,0 +1,123 @@
import logging
import tempfile
from typing import Iterable, Optional, Tuple
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling.datamodel.base_models import OcrCell, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import OcrMacOptions
from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.utils.profiling import TimeRecorder
_log = logging.getLogger(__name__)
class OcrMacModel(BaseOcrModel):
def __init__(self, enabled: bool, options: OcrMacOptions):
super().__init__(enabled=enabled, options=options)
self.options: OcrMacOptions
self.scale = 3 # multiplier for 72 dpi == 216 dpi.
if self.enabled:
setup_errmsg = (
"ocrmac is not correctly installed. "
"Please install it via `pip install ocrmac` to use this OCR engine. "
"Alternatively, Docling has support for other OCR engines. See the documentation."
)
try:
from ocrmac import ocrmac
except ImportError:
raise ImportError(setup_errmsg)
self.reader_RIL = ocrmac.OCR
def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:
if not self.enabled:
yield from page_batch
return
for page_idx, page in enumerate(page_batch):
assert page._backend is not None
if not page._backend.is_valid():
yield page
else:
with TimeRecorder(conv_res, "ocr"):
ocr_rects = self.get_ocr_rects(page)
all_ocr_cells = []
for ocr_rect in ocr_rects:
# Skip zero area boxes
if ocr_rect.area() == 0:
continue
high_res_image = page._backend.get_page_image(
scale=self.scale, cropbox=ocr_rect
)
with tempfile.NamedTemporaryFile(
suffix=".png", mode="w"
) as image_file:
fname = image_file.name
high_res_image.save(fname)
boxes = self.reader_RIL(fname,
recognition_level=self.options.recognition,
framework=self.options.framework,
language_preference=self.options.lang,
).recognize()
im_width, im_height = high_res_image.size
cells = []
for ix, (text, confidence, box) in enumerate(boxes):
x = float(box[0])
y = float(box[1])
w = float(box[2])
h = float(box[3])
x1 = x * im_width
y2 = (1 - y) * im_height
x2 = x1 + w * im_width
y1 = y2 - h * im_height
left = x1 / self.scale
top = y1 / self.scale
right = x2 / self.scale
bottom = y2 / self.scale
cells.append(
OcrCell(
id=ix,
text=text,
confidence=confidence,
bbox=BoundingBox.from_tuple(
coord=(left, top, right, bottom),
origin=CoordOrigin.TOPLEFT,
),
)
)
# del high_res_image
all_ocr_cells.extend(cells)
## Remove OCR cells which overlap with programmatic cells.
filtered_ocr_cells = self.filter_ocr_cells(
all_ocr_cells, page.cells
)
page.cells.extend(filtered_ocr_cells)
# DEBUG code:
if settings.debug.visualize_ocr:
self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects)
yield page

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@ -13,6 +13,7 @@ from docling.datamodel.pipeline_options import (
PdfPipelineOptions,
TesseractCliOcrOptions,
TesseractOcrOptions,
OcrMacOptions,
)
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.ds_glm_model import GlmModel, GlmOptions
@ -26,6 +27,7 @@ from docling.models.page_preprocessing_model import (
from docling.models.table_structure_model import TableStructureModel
from docling.models.tesseract_ocr_cli_model import TesseractOcrCliModel
from docling.models.tesseract_ocr_model import TesseractOcrModel
from docling.models.ocr_mac_model import OcrMacModel
from docling.pipeline.base_pipeline import PaginatedPipeline
from docling.utils.profiling import ProfilingScope, TimeRecorder
@ -118,6 +120,11 @@ class StandardPdfPipeline(PaginatedPipeline):
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
elif isinstance(self.pipeline_options.ocr_options, OcrMacOptions):
return OcrMacModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
return None
def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:

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@ -30,6 +30,7 @@ Works on macOS, Linux, and Windows, with support for both x86_64 and arm64 archi
| [EasyOCR](https://github.com/JaidedAI/EasyOCR) | Default in Docling or via `pip install easyocr`. | `EasyOcrOptions` |
| Tesseract | System dependency. See description for Tesseract and Tesserocr below. | `TesseractOcrOptions` |
| Tesseract CLI | System dependency. See description below. | `TesseractCliOcrOptions` |
| OcrMac | System dependency. See description below. | `OcrMacOptions` |
The Docling `DocumentConverter` allows to choose the OCR engine with the `ocr_options` settings. For example

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@ -89,6 +89,7 @@ torchvision = [
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^0"},
{markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~0.17.2"}
]
ocrmac = {markers = "sys_platform == 'darwin'", version = "^1.0.0"}
[tool.poetry.extras]
tesserocr = ["tesserocr"]