Extract Text from DOCX with IronWord

How Do I Extract All Text from a DOCX Document?

The ExtractText() method retrieves text content from an entire Word document. In this example, we create a new document, add text to it, extract the text using ExtractText(), and display it in the console. This demonstrates the primary text extraction workflow.

The extracted text maintains the logical reading order of the document. The method processes headers, paragraphs, lists, and other text elements in sequence, making it ideal for content analysis and search indexing applications.

:path=/static-assets/word/content-code-examples/how-to/extract-text-simple.cs

using IronWord;

// Instantiate a new DOCX file
WordDocument doc = new WordDocument();

// Add text
doc.AddText("Hello, World!");

// Print extracted text from the document to the console
Console.WriteLine(doc.ExtractText());

What Does the Extracted Text Look Like?

What Output Should I Expect in the Console?

How Can I Extract Text from Specific Paragraphs?

For more control, you can extract text from specific paragraphs instead of the entire document. By accessing the Paragraphs collection, you can target and process any paragraph you need. This granular approach is useful when dealing with documents that have structured content or when you need to process specific sections independently.

In this example, we extract text from the first and last paragraphs, combine them, and save the result to a .txt file. This technique is commonly used in document summarization tools where you might want to extract the introduction and conclusion of a document. Similar to how you might use license keys to unlock features, the Paragraphs collection gives you access to specific document elements.

:path=/static-assets/word/content-code-examples/how-to/extract-text-paragraphs.cs

using IronWord;
using System.IO;

// Load an existing DOCX file
WordDocument doc = new WordDocument("document.docx");

// Extract text and assign variables
string firstParagraph = doc.Paragraphs[0].ExtractText();
string lastParagraph = doc.Paragraphs.Last().ExtractText();

// Combine the texts
string newText = firstParagraph + " " + lastParagraph;

// Export the combined text as a new .txt file
File.WriteAllText("output.txt", newText);

The ability to extract specific paragraphs becomes powerful when combined with document analysis requirements. For instance, you might extract key paragraphs based on their formatting, position, or content patterns. This selective extraction approach helps reduce processing time and focuses on the most relevant content.

What Content Is Extracted from the First Paragraph?

What Content Is Extracted from the Last Paragraph?

How Does the Combined Text Appear in the Output File?

The screenshots above show the first paragraph extraction, last paragraph extraction, and the combined output saved to a text file. Notice how the extraction process preserves the text content while removing formatting information, making it suitable for plain text processing.

How Do I Extract Data from Tables in DOCX?

Tables often contain structured data that needs to be extracted for processing or analysis. IronWord allows you to access table data by navigating through rows and cells. In this example, we load a document containing an API statistics table and extract a specific cell value from the 4th column of the 2nd row.

Table extraction is essential for data migration projects, report generation, and automated data collection workflows. When working with tabular data, understanding the zero-based indexing system is crucial - the first table is Tables[0], the first row is Rows[0], and so on. This systematic approach, similar to licensing structures, provides predictable access patterns.

:path=/static-assets/word/content-code-examples/how-to/extract-text-table.cs

using IronWord;

// Load the API statistics document
WordDocument apiStatsDoc = new WordDocument("api-statistics.docx");

// Extract text from the 1st table, 4th column and 2nd row
string extractedValue = apiStatsDoc.Tables[0].Rows[2].Cells[3].ExtractText();

// Print extracted value
Console.WriteLine($"Target success rate: {extractedValue}");

What Does the Source Table Look Like?

What Value Is Retrieved from the Table Cell?

Advanced Text Extraction Scenarios

When working with complex documents, you may need to combine multiple extraction techniques. Here's an example that demonstrates extracting text from multiple elements and processing them differently:

using IronWord;
using System.Text;
using System.Linq;

// Load a complex document
WordDocument complexDoc = new WordDocument("report.docx");

// Create a StringBuilder for efficient string concatenation
StringBuilder extractedContent = new StringBuilder();

// Extract and process headers (assuming they're in the first few paragraphs)
var headers = complexDoc.Paragraphs
    .Take(3)
    .Select(p => p.ExtractText())
    .Where(text => !string.IsNullOrWhiteSpace(text));

foreach (var header in headers)
{
    extractedContent.AppendLine($"HEADER: {header}");
}

// Extract table summaries
foreach (var table in complexDoc.Tables)
{
    // Get first cell as table header/identifier
    string tableIdentifier = table.Rows[0].Cells[0].ExtractText();
    extractedContent.AppendLine($"\nTABLE: {tableIdentifier}");

    // Extract key metrics (last row often contains totals)
    if (table.Rows.Count > 1)
    {
        var lastRow = table.Rows.Last();
        var totals = lastRow.Cells.Select(cell => cell.ExtractText());
        extractedContent.AppendLine($"Totals: {string.Join(", ", totals)}");
    }
}

// Save the structured extraction
System.IO.File.WriteAllText("structured-extract.txt", extractedContent.ToString());

using IronWord;
using System.Text;
using System.Linq;

// Load a complex document
WordDocument complexDoc = new WordDocument("report.docx");

// Create a StringBuilder for efficient string concatenation
StringBuilder extractedContent = new StringBuilder();

// Extract and process headers (assuming they're in the first few paragraphs)
var headers = complexDoc.Paragraphs
    .Take(3)
    .Select(p => p.ExtractText())
    .Where(text => !string.IsNullOrWhiteSpace(text));

foreach (var header in headers)
{
    extractedContent.AppendLine($"HEADER: {header}");
}

// Extract table summaries
foreach (var table in complexDoc.Tables)
{
    // Get first cell as table header/identifier
    string tableIdentifier = table.Rows[0].Cells[0].ExtractText();
    extractedContent.AppendLine($"\nTABLE: {tableIdentifier}");

    // Extract key metrics (last row often contains totals)
    if (table.Rows.Count > 1)
    {
        var lastRow = table.Rows.Last();
        var totals = lastRow.Cells.Select(cell => cell.ExtractText());
        extractedContent.AppendLine($"Totals: {string.Join(", ", totals)}");
    }
}

// Save the structured extraction
System.IO.File.WriteAllText("structured-extract.txt", extractedContent.ToString());

This advanced example shows how to create structured extractions by combining different document elements. This approach is useful for generating document summaries, creating indexes, or preparing data for further processing. Just as upgrades enhance software capabilities, combining extraction methods enhances your document processing capabilities.

Best Practices for Text Extraction

When implementing text extraction in production applications, consider these best practices:

1. Error Handling: Always wrap extraction code in try-catch blocks to handle documents that might be corrupted or have unexpected structures.

2. Performance Optimization: For large documents or batch processing, consider extracting only the necessary portions rather than the entire document content.

3. Character Encoding: Be aware of character encoding when saving extracted text, especially for documents containing special characters or multiple languages.

4. Memory Management: When processing multiple documents, properly dispose of WordDocument objects to prevent memory leaks.

Remember that text extraction preserves the logical reading order but removes formatting. If you need to maintain formatting information, consider using additional IronWord features or storing metadata separately. For production deployments, review the changelog to stay updated with the latest features and improvements.

Summary

IronWord's ExtractText() method provides a powerful and flexible way to extract text from DOCX files. Whether you need to extract entire documents, specific paragraphs, or table data, the API offers straightforward methods to accomplish your goals. By combining these techniques with proper error handling and optimization strategies, you can build robust document processing applications that efficiently handle various text extraction scenarios.

For more advanced scenarios and to explore additional features, check out extensions and other documentation resources to enhance your document processing capabilities.