native_ocr_py 0.0.1

Call macOS/Windows built-in Vision OCR engine via Python C extension

native_ocr_py

A Python library that exposes the macOS Vision OCR engine via a Python C extension — no third-party OCR service, no bundled models, no network calls. Uses the OS-native framework directly for fast, on-device text recognition.

Platform support: macOS only for now. Windows support is planned.

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Requirements

• macOS 13 or later (for Vision framework revision 3)
• Python 3.11 or later

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Installation

Install from PyPI:

pip install native_ocr_py

Because the package includes a compiled C extension, a pre-built wheel for your macOS architecture (arm64 / x86_64) must be available on PyPI. If no matching wheel is found, pip will attempt to build from source, which requires Xcode Command Line Tools:

xcode-select --install

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Quick start

import asyncio
import native_ocr

async def main():
    with open("screenshot.png", "rb") as f:
        data = f.read()

    results = await native_ocr.perform_ocr_on_image(data, normalized=True)

    for r in results:
        print(r.content, r.position)

asyncio.run(main())

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API

get_supported_languages() -> list[str]

Returns BCP-47 language codes supported by the Vision OCR engine (e.g. "en-US", "zh-Hans"). The result is cached after the first call.

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await perform_ocr_on_image(data, normalized, *, roi, high_accuracy, languages, custom_words) -> list[OcrResult]

Run OCR on an encoded image loaded into memory. Accepts any format supported by the OS decoder — JPEG and PNG are guaranteed; HEIC, TIFF, BMP, and WebP are available on most systems.

Parameter	Type	Default	Description
data	bytes	—	Raw bytes of an encoded image file
normalized	bool	—	True → result coordinates in [0.0, 1.0]; False → pixels
roi	BoundingBox | None	None	Region of interest in normalised coordinates. None = full image
high_accuracy	bool	True	Use the accurate (slower) recognition level
languages	list[str] | None	None	BCP-47 hints from get_supported_languages(). None = auto-detect
custom_words	list[str] | None	None	Domain-specific vocabulary hints. Only applied when high_accuracy=True

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await perform_ocr_on_bgra(bgra, width, height, normalized, *, roi, high_accuracy, languages, custom_words) -> list[OcrResult]

Run OCR on a raw BGRA8 pixel buffer. Useful when you already have decoded pixel data (e.g. from a screen capture or camera frame) and want to avoid re-encoding.

The buffer must be tightly packed: len(bgra) == width * height * 4.

Parameter	Type	Default	Description
bgra	bytes	—	Raw BGRA8 pixel data, no row padding
width	int	—	Image width in pixels
height	int	—	Image height in pixels
normalized	bool	—	True → result coordinates in [0.0, 1.0]; False → pixels
roi	BoundingBox | None	None	Region of interest in normalised coordinates. None = full image
high_accuracy	bool	True	Use the accurate (slower) recognition level
languages	list[str] | None	None	BCP-47 hints. None = auto-detect
custom_words	list[str] | None	None	Domain-specific vocabulary hints. Only applied when high_accuracy=True

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BoundingBox

@dataclass
class BoundingBox:
    x: float      # distance from left edge
    y: float      # distance from top edge
    width: float
    height: float

Top-left-origin coordinate rectangle. When used as roi input, always normalised. When returned in OcrResult, normalised or pixel depending on the normalized flag.

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OcrResult

@dataclass
class OcrResult:
    content: str        # recognised text, stripped, never empty
    position: BoundingBox

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Examples

Restrict to a region of interest

# Scan only the top-right quarter of the image
roi = native_ocr.BoundingBox(x=0.5, y=0.0, width=0.5, height=0.5)
results = await native_ocr.perform_ocr_on_image(data, normalized=True, roi=roi)

Pixel coordinates

results = await native_ocr.perform_ocr_on_image(data, normalized=False)
for r in results:
    print(f"{r.content!r} at ({r.position.x:.0f}, {r.position.y:.0f})")

OCR from a raw screen capture buffer

async def ocr_frame(bgra_bytes: bytes, width: int, height: int):
    return await native_ocr.perform_ocr_on_bgra(
        bgra_bytes, width, height,
        normalized=True,
        high_accuracy=False,   # faster for real-time use
    )

Language hints

langs = native_ocr.get_supported_languages()
print(langs)  # ['en-US', 'zh-Hans', 'zh-Hant', 'ja-JP', ...]

results = await native_ocr.perform_ocr_on_image(
    data, normalized=True, languages=["zh-Hans", "en-US"]
)

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License

MIT