What data is extracted when converting DOCX to JSON?

The converter extracts paragraphs with their text and formatting, tables with all cell data, document metadata, style information, and generates statistics like word count and character count.

Can I use the JSON output in my application?

Yes, the JSON output is standard-compliant and can be easily parsed by any programming language. It's perfect for APIs, data processing, content management systems, and search indexing.

Does the converter preserve formatting information?

Yes, the converter preserves text formatting like bold, italic, and underline, as well as paragraph styles, heading levels, and table structure in the JSON output.

Convert DOCX to JSON

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Max file size 100mb.

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DOCX vs JSON Format Comparison

Aspect	DOCX (Source Format)	JSON (Target Format)
Format Overview	DOCX Office Open XML Document Microsoft Word's document format designed for word processing, with rich formatting, layouts, and multimedia support. Binary/XML Format Document-Focused	JSON JavaScript Object Notation Lightweight data interchange format that's human-readable and machine-parsable. Perfect for APIs, configurations, and data storage. Text Format Data-Focused
Technical Specifications	Structure: ZIP archive with XML files Encoding: UTF-8/UTF-16 Media: Embedded images/objects Schema: Office Open XML Extensions: .docx, .docm	Structure: Key-value pairs, arrays Encoding: UTF-8 Media: Base64 or URLs Schema: RFC 8259 standard Extensions: .json
Data Structure	Paragraphs with styles Formatted text runs Complex table layouts Headers and footers Comments and revisions Embedded media files Document properties	Objects and arrays Key-value pairs Nested structures String, number, boolean types Null values No comments (pure data) Metadata as properties
Advantages	Rich formatting options Professional layouts WYSIWYG editing Print-ready documents Collaborative features	Language agnostic Easy to parse and generate Lightweight and fast Native JavaScript support API-friendly format
Disadvantages	Requires specific software Complex internal structure Not human-readable raw Larger file sizes	No native formatting No visual layout No comments allowed Strict syntax rules
Common Uses	Business documents Reports and proposals Academic papers Legal documents Professional correspondence	REST APIs Configuration files Data exchange Database exports Application settings
Data Extraction	From DOCX Document: Paragraphs with formatting Heading hierarchy (H1-H6) Tables with cell data Lists (bullet & numbered) Text styles (bold, italic) Document metadata	To JSON Structure: Nested objects and arrays Text content preservation Style information as properties Table data as 2D arrays Word/character statistics Document properties object

Why Convert DOCX to JSON?

Converting DOCX to JSON enables programmatic access to document content, making it perfect for data processing, content management systems, search indexing, and API integration. The structured JSON output preserves document hierarchy and formatting information.

JSON Output Structure:

{
  "metadata": {
    "source_file": "document.docx",
    "conversion_time": "2024-01-01 12:00:00",
    "document_properties": {
      "paragraphs_count": 25,
      "tables_count": 3
    }
  },
  "content": {
    "paragraphs": [
      {
        "index": 0,
        "text": "Document Title",
        "style": "Heading 1",
        "type": "heading",
        "level": 1,
        "runs": [
          {
            "text": "Document Title",
            "formatting": {
              "bold": true,
              "italic": false,
              "underline": false
            }
          }
        ]
      }
    ],
    "tables": [
      {
        "index": 0,
        "rows_count": 3,
        "columns_count": 4,
        "data": [...]
      }
    ]
  },
  "statistics": {
    "total_words": 500,
    "total_characters": 2500,
    "total_headings": 5
  }
}

Use Cases:

Content Management: Extract document content for CMS integration
Data Analysis: Process document text and statistics programmatically
Search Indexing: Index document content for full-text search
API Integration: Send document data to web services
Migration: Move content between different systems
Automation: Process documents in automated workflows

Best Practices:

Use consistent heading styles in Word for proper hierarchy
Keep table structures simple for cleaner JSON output
Review JSON structure for your specific use case
Consider JSON schema validation for data integrity
Use JSON parsing libraries for processing the output

Working with JSON Output:

JavaScript Example:

const docData = JSON.parse(jsonString);
// Access document paragraphs
docData.content.paragraphs.forEach(para => {
    console.log(para.text);
});
// Access table data
docData.content.tables.forEach(table => {
    console.log(`Table has ${table.rows_count} rows`);
});

Python Example:

import json

with open('document.json', 'r') as file:
    doc_data = json.load(file)
    
# Access paragraphs
for para in doc_data['content']['paragraphs']:
    print(para['text'])
    
# Get statistics
stats = doc_data['statistics']
print(f"Total words: {stats['total_words']}")