natocr 1.4.0

Native OCR library using platform-specific frameworks (macOS Vision, Windows Runtime OCR)

natocr

natocr (native ocr) is a small Python wrapper around the OCR engines that already ship with macOS and Windows: Vision framework on macOS and Windows Runtime OCR on Windows.

These built-in engines are generally faster, more efficient, and more accurate than third-party alternatives like Tesseract. natocr makes reaching for them painless via one clean Python API instead of wrangling with Objective-C bridges or WinRT async plumbing.

Install

pip install natocr[macos]      # on macOS
pip install natocr[windows]    # on Windows

Quick start

from natocr import OCR

ocr = OCR()                    # defaults to english
result = ocr.recognize("invoice.png")

print(result.text)

Invoice #1042 Total $58.20 Thank you!

Confidence Scores and Bounding Boxes

recognize() returns an OCRResult. Beyond the flat .text, you get a per-detection breakdown with bounding boxes and (on macOS) confidence scores:

result = ocr.recognize("receipt.png")

print(result.confidence)          # average confidence, or None if unavailable

for element in result.elements:
    box = element.bounds.bounds   # (x, y, width, height) in pixels
    print(f"{element.text!r} @ {box} conf={element.confidence}")

0.93
'Acme Coffee' @ (24.0, 18.0, 180.0, 32.0) conf=0.97
'Latte' @ (24.0, 70.0, 96.0, 28.0) conf=0.95
'$4.50' @ (220.0, 70.0, 80.0, 28.0) conf=0.88

Lines and Words

There's also convenience views for grouping results by reading order:

result.lines      # ['Acme Coffee', 'Latte $4.50']  - elements grouped into lines
result.words      # list of TextElement with non-empty text

Detection Language

Pick a different recognition language, and inspect what the current platform supports:

ocr = OCR(language="fr")
print(ocr.platform)               # 'darwin' or 'win32'
print(ocr.supported_languages)    # ['en-US', 'fr-FR', 'de-DE', ...]

The supported set is decided by the OS and queried live, so supported_languages always reflects the current machine. On macOS it's Vision's built-in set for your macOS version; on Windows it's whatever OCR language packs are installed. See the Usage guide for the full list and how to add Windows language packs.

Alternative Inputs

recognize() accepts more than file paths - hand it whatever you already have in memory:

from PIL import Image
import numpy as np

ocr.recognize("page.png")              # a file path
ocr.recognize(Image.open("page.png"))  # a PIL image
ocr.recognize(np.array(image))         # a numpy array (e.g. from OpenCV)
ocr.recognize(open("page.png", "rb").read())  # raw image bytes

Supported File Types

Images are decoded with Pillow, so any raster format Pillow can open works as an input file or byte string. HEIC/HEIF decoding is provided by the bundled pillow-heif, so iPhone photos work with no extra setup.

Format	Extensions	Notes
PNG	.png	recommended - lossless
JPEG	.jpg, .jpeg	great for photos of documents
TIFF	.tif, .tiff	common for scans
BMP	.bmp	uncompressed bitmap
GIF	.gif	first frame is used
WebP	.webp	modern lossy/lossless
HEIC/HEIF	.heic, .heif, .hif	iPhone photos and screenshots
PPM/PGM	.ppm, .pgm	netpbm bitmaps

In addition to file paths, recognize() accepts these in-memory types:

Input type	Example
str (file path)	ocr.recognize("page.png")
PIL.Image.Image	ocr.recognize(Image.open("page.png"))
numpy.ndarray	ocr.recognize(np.array(image))
bytes (encoded image)	ocr.recognize(data)

[!NOTE] PDFs and other multi-page documents aren't decoded directly - rasterize a page to one of the formats above first (e.g. with pdf2image or pymupdf).

Testing

Install the dev dependencies (in a virtualenv), then run the suite. The tests mock the native macOS Vision and Windows Runtime backends, so they run anywhere without those frameworks installed.

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Run everything with coverage (coverage is wired up in pyproject.toml, so plain pytest already reports it):

pytest

Other handy invocations:

# run a single test file
pytest tests/test_models.py

# run one test by name
pytest -k test_lines_groups_close_y_into_single_line

# verbose output
pytest -v

Coverage reports land in the terminal, in htmlcov/index.html, and in coverage.xml.