doc23 0.1.1

Powerful Python library to convert documents (PDF, DOCX, TXT) into structured JSON trees for legal, institutional, and NLP applications.

📚 doc23

Convert documents into structured JSON effortlessly.
A Python library for extracting text from various document formats and structuring it hierarchically into JSON.

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📌 Features

• ✅ Extract text from PDFs, DOCX, TXT, RTF, ODT, MD, and images.
• 🖼️ OCR support for scanned documents and images.
• ⚙️ Flexible configuration using regex patterns and field mapping.
• 🌳 Nested hierarchical structure output in JSON.
• ✨ Explicit leaf-level control using is_leaf=True.
• 🔍 Built-in validations to catch config mistakes (regex, hierarchy, field conflicts).
• 🧪 Comprehensive pytest suite with coverage reporting.

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📦 Installation

pip install doc23

To enable OCR:

sudo apt install tesseract-ocr
pip install pytesseract

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🚀 Quickstart Example

Basic Text Extraction

from doc23 import extract_text

# Extract text from any supported document
text = extract_text("document.pdf", scan_or_image="auto")
print(text)

Structured Document Parsing

from doc23 import Doc23, Config, LevelConfig

config = Config(
    root_name="art_of_war",
    sections_field="chapters",
    description_field="description",
    levels={
        "chapter": LevelConfig(
            pattern=r"^CHAPTER\s+([IVXLCDM]+)\n(.+)$",
            name="chapter",
            title_field="title",
            description_field="description",
            sections_field="paragraphs"
        ),
        "paragraph": LevelConfig(
            pattern=r"^(\d+)\.\s+(.+)$",
            name="paragraph",
            title_field="number",
            description_field="text",
            is_leaf=True
        )
    }
)

with open("art_of_war.txt") as f:
    text = f.read()

doc = Doc23(text, config)
structure = doc.prune()

print(structure["chapters"][0]["title"])  # → I

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🧾 Output Example

{
  "description": "",
  "chapters": [
    {
      "type": "chapter",
      "title": "I",
      "description": "Laying Plans",
      "paragraphs": [
        {
          "type": "paragraph",
          "number": "1",
          "text": "Sun Tzu said: The art of war is of vital importance to the State."
        }
      ]
    }
  ]
}

---

🛠️ Document Configuration

Use Config and LevelConfig to define how your document is parsed:

Field	Purpose
pattern	Regex to match each level
title_field	Field to assign the first regex group
description_field	(Optional) Field for second group
sections_field	(Optional) Where sublevels go
paragraph_field	(Optional) Where text/nodes go if leaf
is_leaf	(Optional) Forces this level to be terminal

Capture Group Rules

Fields Defined	Required Groups in Regex
title_field only	≥1
title_field + description_field	≥2
title_field + paragraph_field	≥1 (second group optional)

---

🏗️ Architecture Overview

doc23 consists of several key components:

Doc23 (core.py)
├── Extractors (extractors/)
│   ├── PDFExtractor
│   ├── DocxExtractor
│   ├── TextExtractor
│   └── ...
├── Config (config_tree.py)
│   └── LevelConfig
└── Gardener (gardener.py)

1. Doc23: Main entry point, handles file detection and orchestration
2. Extractors: Convert various document types to plain text
3. Config: Defines how to structure the document hierarchy
4. Gardener: Parses text and builds the JSON structure

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✅ Built-in Validation

The library validates your config when creating Doc23:

• ✋ Ensures all parents exist.
• 🔁 Detects circular relationships.
• ⚠️ Checks field name reuse.
• 🧪 Verifies group counts match pattern.

If any issue is found, a ValueError will be raised immediately.

---

🧪 Testing

The library includes a comprehensive test suite covering various scenarios:

Basic Initialization

def test_gardener_initialization():
    config = Config(
        root_name="document",
        sections_field="sections",
        description_field="description",
        levels={
            "book": LevelConfig(
                pattern=r"^BOOK\s+(.+)$",
                name="book",
                title_field="title",
                description_field="description",
                sections_field="sections"
            ),
            "article": LevelConfig(
                pattern=r"^ARTICLE\s+(\d+)\.\s*(.*)$",
                name="article",
                title_field="title",
                description_field="content",
                paragraph_field="paragraphs",
                parent="book"
            )
        }
    )
    gardener = Gardener(config)
    assert gardener.leaf == "article"

Document Structure

def test_prune_basic_structure():
    config = Config(
        root_name="document",
        sections_field="sections",
        description_field="description",
        levels={
            "book": LevelConfig(
                pattern=r"^BOOK\s+(.+)$",
                name="book",
                title_field="title",
                description_field="description",
                sections_field="sections"
            ),
            "article": LevelConfig(
                pattern=r"^ARTICLE\s+(\d+)\.\s*(.*)$",
                name="article",
                title_field="title",
                description_field="content",
                paragraph_field="paragraphs",
                parent="book"
            )
        }
    )
    gardener = Gardener(config)
    text = """BOOK First Book
This is a description
ARTICLE 1. First article
This is article content
More content"""
    result = gardener.prune(text)
    assert result["sections"][0]["title"] == "First Book"
    assert result["sections"][0]["sections"][0]["paragraphs"] == ["This is article content", "More content"]

Edge Cases

def test_prune_empty_document():
    config = Config(
        root_name="document",
        sections_field="sections",
        description_field="description",
        levels={}
    )
    gardener = Gardener(config)
    result = gardener.prune("")
    assert result["sections"] == []

Free Text Handling

def test_prune_with_free_text():
    config = Config(
        root_name="document",
        sections_field="sections",
        description_field="description",
        levels={
            "title": LevelConfig(
                pattern=r"^TITLE\s+(.+)$",
                name="title",
                title_field="title",
                description_field="description",
                sections_field="sections"
            )
        }
    )
    gardener = Gardener(config)
    text = """This is free text at the top level
TITLE First Title
Title description"""
    result = gardener.prune(text)
    assert result["description"] == "This is free text at the top level"

Run tests with:

python -m pytest tests/

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❓ Troubleshooting FAQ

OCR not working

Make sure Tesseract is installed and accessible in your PATH.

Text extraction issues

Different document formats may require specific libraries. Check your dependencies:

• PDF: pdfplumber, pdf2image
• DOCX: docx2txt
• ODT: odf

Regex pattern not matching

Test your patterns with tools like regex101.com and ensure you have the correct number of capture groups.

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🔄 Compatibility

• Python 3.8+
• Tested on Linux, macOS, and Windows

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👥 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

MIT

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🔗 Resources

• Project Gutenberg – Public Domain Texts
• Tesseract OCR Wiki
• GitHub Repository

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🧠 Advanced Usage

For advanced patterns, dynamic configs, exception handling and OCR examples, see:

📄 ADVANCED_USAGE_doc23.md