<div align=center>
  <h1>PDF Craft</h1>
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  <p>English | <a href="./README_zh-CN.md">中文</a></p>
</div>

## Introduction

pdf-craft converts PDF files into various other formats, with a focus on handling scanned book PDFs.

This project is based on [DeepSeek OCR](https://github.com/deepseek-ai/DeepSeek-OCR) for document recognition. It supports the recognition of complex content such as tables and formulas. With GPU acceleration, pdf-craft can complete the entire conversion process from PDF to Markdown or EPUB locally. During the conversion, pdf-craft automatically identifies document structure, accurately extracts body text, and filters out interfering elements like headers and footers. For academic or technical documents containing footnotes, formulas, and tables, pdf-craft handles them properly, preserving these important elements. The final Markdown or EPUB files maintain the content integrity and readability of the original book.

## Lightweight and Fast

Starting from the official v1.0.0 release, pdf-craft fully embraces [DeepSeek OCR](https://github.com/deepseek-ai/DeepSeek-OCR) and no longer relies on LLM for text correction. This change brings significant performance improvements: the entire conversion process is completed locally without network requests, eliminating the long waits and occasional network failures of the old version.

However, the new version has also removed the LLM text correction feature. If your use case still requires this functionality, you can continue using the old version [v0.2.8](https://github.com/oomol-lab/pdf-craft/tree/v0.2.8).

## Quick Start

### Installation

```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install pdf-craft
```

This project uses DeepSeek OCR, which depends on a CUDA environment. **The above command only ensures Python can read types without errors, but cannot actually run OCR recognition.** For specific CUDA environment installation instructions, please refer to the [Installation Guide](docs/INSTALLATION.md).

### Quick Start

#### Convert to Markdown

```python
from pdf_craft import transform_markdown

transform_markdown(
    pdf_path="input.pdf",
    markdown_path="output.md",
    markdown_assets_path="images",
)
```

![](docs/images/pdf2md-en.png)

#### Convert to EPUB

```python
from pdf_craft import transform_epub, BookMeta

transform_epub(
    pdf_path="input.pdf",
    epub_path="output.epub",
    book_meta=BookMeta(
        title="Book Title",
        authors=["Author"],
    ),
)
```

![](docs/images/pdf2epub-en.png)

## Detailed Usage

### Convert to Markdown

```python
from pdf_craft import transform_markdown

transform_markdown(
    pdf_path="input.pdf",
    markdown_path="output.md",
    markdown_assets_path="images",
    analysing_path="temp",  # Optional: specify temporary folder
    model="gundam",  # Optional: tiny, small, base, large, gundam
    models_cache_path="models",  # Optional: model cache path
    includes_footnotes=True,  # Optional: include footnotes
    generate_plot=False,  # Optional: generate visualization charts
)
```

### Convert to EPUB

```python
from pdf_craft import transform_epub, BookMeta, TableRender, LaTeXRender

transform_epub(
    pdf_path="input.pdf",
    epub_path="output.epub",
    analysing_path="temp",  # Optional: specify temporary folder
    model="gundam",  # Optional: OCR model size
    models_cache_path="models",  # Optional: model cache path
    includes_cover=True,  # Optional: include cover
    includes_footnotes=True,  # Optional: include footnotes
    generate_plot=False,  # Optional: generate visualization charts
    book_meta=BookMeta(
        title="Book Title",
        authors=["Author 1", "Author 2"],
        publisher="Publisher",
        language="en",
    ),
    lan="en",  # Optional: language (zh/en)
    table_render=TableRender.HTML,  # Optional: table rendering method
    latex_render=LaTeXRender.MATHML,  # Optional: formula rendering method
)
```

### Model Management

pdf-craft depends on DeepSeek OCR models, which are automatically downloaded from Hugging Face on first run. You can control model storage and loading behavior through the `models_cache_path` and `local_only` parameters.

#### Pre-download Models

In production environments, it is recommended to download models in advance to avoid downloading on first run:

```python
from pdf_craft import predownload_models

predownload_models(
    models_cache_path="models",  # Specify model cache directory
    revision=None,  # Optional: specify model version
)
```

#### Specify Model Cache Path

By default, models are downloaded to the system's Hugging Face cache directory. You can customize the cache location through the `models_cache_path` parameter:

```python
from pdf_craft import transform_markdown

transform_markdown(
    pdf_path="input.pdf",
    markdown_path="output.md",
    models_cache_path="./my_models",  # Custom model cache directory
)
```

#### Offline Mode

If you have pre-downloaded the models, you can use `local_only=True` to disable network downloads and ensure only local models are used:

```python
from pdf_craft import transform_markdown

transform_markdown(
    pdf_path="input.pdf",
    markdown_path="output.md",
    models_cache_path="./my_models",
    local_only=True,  # Use local models only, do not download from network
)
```

## API Reference

### OCR Models

Supports the following DeepSeek OCR models:

- `tiny` - Smallest model, fastest speed
- `small` - Small model
- `base` - Base model
- `large` - Large model
- `gundam` - Largest model, highest quality (default)

### Table Rendering Methods

- `TableRender.HTML` - HTML format (default)
- `TableRender.MARKDOWN` - Markdown format
- `TableRender.TEXT` - Plain text format

### Formula Rendering Methods

- `LaTeXRender.MATHML` - MathML format (default)
- `LaTeXRender.IMAGE` - Image format
- `LaTeXRender.TEXT` - Plain text format

## Related Open Source Libraries
[epub-translator](https://github.com/oomol-lab/epub-translator) uses AI large language models to automatically translate EPUB e-books while 100% preserving the original book's format, illustrations, table of contents, and layout. It also generates bilingual versions for convenient language learning or international sharing. When combined with this library, you can convert and translate scanned PDF books. For a demonstration, see this [video: Convert PDF scanned books to EPUB format and translate to bilingual books](https://www.bilibili.com/video/BV1tMQZY5EYY).

## License

This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.

Starting from v1.0.0, pdf-craft has fully migrated to DeepSeek OCR (MIT license), removing the previous AGPL-3.0 dependency, allowing the entire project to be released under the more permissive MIT license. Thanks to the community for their support and contributions!

## Acknowledgments

- [DeepSeekOCR](https://github.com/deepseek-ai/DeepSeek-OCR)
- [doc-page-extractor](https://github.com/Moskize91/doc-page-extractor)
