doc-page-extractor

Document page extraction tool that converts page images into text layouts with pixel coordinates.

The default backend remains local DeepSeek-OCR for existing users. Version 1.1 adds a unified OCR adapter layer with DeepSeek OpenAI-compatible Vendor support and Baidu cloud OCR support.

Installation

pip install doc-page-extractor

PyTorch is not installed automatically. You only need CUDA PyTorch when using the local DeepSeek-OCR backend.

Backends

Local DeepSeek-OCR

This is the default and keeps the existing API behavior:

from doc_page_extractor import create_page_extractor

extractor = create_page_extractor()

Install CUDA PyTorch before using this backend:

# CUDA 12.1
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

# CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# CUDA 12.6
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126

Check CUDA with:

nvidia-smi
python -c "import torch; print(torch.cuda.is_available())"

DeepSeek OCR Vendor

Use this backend when DeepSeek OCR is exposed through an OpenAI-compatible endpoint:

from doc_page_extractor import (
    DeepSeekVendorOCRConfig,
    create_deepseek_vendor_page_extractor,
)

extractor = create_deepseek_vendor_page_extractor(
    DeepSeekVendorOCRConfig.from_env()
)

Expected environment variables:

DOC_PAGE_EXTRACTOR_DEEPSEEK_VENDOR_BASE_URL=
DOC_PAGE_EXTRACTOR_DEEPSEEK_VENDOR_API_KEY=
DOC_PAGE_EXTRACTOR_DEEPSEEK_VENDOR_MODEL=deepseek-ocr
DOC_PAGE_EXTRACTOR_DEEPSEEK_VENDOR_TEMPERATURE=0.0
DOC_PAGE_EXTRACTOR_DEEPSEEK_VENDOR_TOP_P=0.7

Baidu Cloud OCR

Use this backend for Baidu Unlimited-OCR through Baidu Cloud:

from doc_page_extractor import BaiduCloudOCRConfig, create_baidu_page_extractor

extractor = create_baidu_page_extractor(BaiduCloudOCRConfig.from_env())

Expected environment variables:

DOC_PAGE_EXTRACTOR_BAIDU_AK=
DOC_PAGE_EXTRACTOR_BAIDU_SK=
DOC_PAGE_EXTRACTOR_BAIDU_BASE_URL=https://aip.baidubce.com

Extraction

All backends return the same PageExtractor shape:

from PIL import Image
from doc_page_extractor import ExtractionContext

context = ExtractionContext(check_aborted=lambda: False)

for page_image, layouts in extractor.extract(
    image=Image.open("page.png"),
    size="gundam",
    stages=1,
    context=context,
):
    for layout in layouts:
        print(layout.det, layout.text)

Layout keeps the original ref, det, and text fields. Version 1.1.1 also adds kind, a stable LayoutKind enum that callers should prefer over provider-specific labels. Adapter metadata remains available through optional fields such as type, polygon, html, source, and raw.

Use extract_page_results() when you need the structured page model:

from doc_page_extractor import LayoutKind

for page_image, result in extractor.extract_page_results(
    image=Image.open("page.png"),
    size="gundam",
    stages=1,
    context=context,
):
    if result.structured is None:
        continue
    for block in result.structured.blocks:
        if block.kind == LayoutKind.TABLE:
            print(block.html)

The structured model groups asset captions with images, tables, and equations when possible. DeepSeek output is structured from flat OCR tags; Baidu Cloud OCR is normalized from Baidu's richer layout JSON into the same public kinds.

Baidu Cloud OCR extracts footnotes directly. If stages > 1 is requested with the Baidu adapter, the extractor emits a warning and runs a single stage because DeepSeek-style multi-stage redaction can erase Baidu footnote regions.

Development

For contributors and developers, see Development Guide.

Useful local commands:

poetry run python test.py
poetry run pylint --disable=import-error doc_page_extractor
poetry run python scripts/ocr_sample.py --adapter both --image tests/images/friendly-title.png

Requirements

• Python >= 3.10, < 3.14
• CUDA-capable NVIDIA GPU only when using local DeepSeek-OCR
• Remote OCR credentials only when using DeepSeek Vendor or Baidu Cloud OCR

Dependencies & Licenses

This project is licensed under the MIT License. The local DeepSeek-OCR backend depends on the DeepSeek-OCR model, which uses easydict (LGPLv3) for configuration management.