Table to Text
How to Extract Table Data from Images with Aspose.OCR

How to Extract Table Data from Images with Aspose.OCR

Extracting tables from scanned or photographed images is often a manual, error-prone process. With Aspose.OCR Table to Text for .NET, you can automate the extraction of structured table data from images—saving time, reducing errors, and enabling seamless integration with databases, Excel, or reporting tools.

Real-World Problem

Businesses frequently receive tables in invoices, reports, or forms as images or scans. Manually re-entering this data into spreadsheets or analytics platforms is inefficient and error-prone, especially for large volumes or complex tables.

Solution Overview

Aspose.OCR Table to Text for .NET automates table recognition and data extraction from images, accurately identifying cell structure and content. This lets you transform scanned or photographed tables into structured, searchable, and editable formats with minimal code.

Prerequisites

Before starting, you’ll need:

  1. Visual Studio 2019 or later
  2. .NET 6.0 or later (or .NET Framework 4.6.2+)
  3. Aspose.OCR for .NET from NuGet
  4. Basic C# knowledge
PM> Install-Package Aspose.OCR

Step-by-Step Implementation

Step 1: Install and Configure Aspose.OCR

Add the Aspose.OCR package and include the necessary namespaces:

using Aspose.OCR;

Step 2: Prepare Table Image Inputs

Add one or more table images to your input. For batch extraction, use multiple files.

OcrInput input = new OcrInput(InputType.SingleImage);
input.Add("table1.png");
input.Add("table2.jpg");

Step 3: Configure Table Recognition Settings

Enable table detection mode to ensure the structure is recognized accurately.

RecognitionSettings settings = new RecognitionSettings();
settings.DetectAreasMode = DetectAreasMode.TABLE;
settings.Language = Language.English; // Adjust if table contains non-English text

Step 4: Run the Table Recognition Process

Recognize tables with the configured settings:

AsposeOcr ocr = new AsposeOcr();
List<RecognitionResult> results = ocr.Recognize(input, settings);

Step 5: Export and Use Table Data

Save or process the recognized table data. You can export to text, Excel, JSON, or other formats.

foreach (RecognitionResult result in results)
{
    Console.WriteLine(result.RecognitionText); // Raw table as text
    result.Save("table.csv", SaveFormat.Csv); // Save as CSV
    result.Save("table.xlsx", SaveFormat.Xlsx); // Save as Excel
}

Step 6: Add Error Handling

Add exception handling to build robust solutions.

try
{
    AsposeOcr ocr = new AsposeOcr();
    List<RecognitionResult> results = ocr.Recognize(input, settings);
    // further processing...
}
catch (Exception ex)
{
    Console.WriteLine($"Error: {ex.Message}");
}

Step 7: Optimize for Complex Tables

  • Use high-resolution scans/photos for accurate structure detection
  • Test with various table layouts (merged cells, multi-line headers, borders)
  • Tune recognition settings as needed
// Example: Add all images from a folder
foreach (string file in Directory.GetFiles("./tables", "*.png"))
{
    input.Add(file);
}

Step 8: Complete Working Example 

using Aspose.OCR;
using System;
using System.Collections.Generic;

class Program
{
    static void Main(string[] args)
    {
        try
        {
            OcrInput input = new OcrInput(InputType.SingleImage);
            input.Add("table1.png");
            input.Add("table2.jpg");

            RecognitionSettings settings = new RecognitionSettings();
            settings.DetectAreasMode = DetectAreasMode.TABLE;
            settings.Language = Language.English;

            AsposeOcr ocr = new AsposeOcr();
            List<RecognitionResult> results = ocr.Recognize(input, settings);

            foreach (RecognitionResult result in results)
            {
                Console.WriteLine(result.RecognitionText);
                result.Save("table.csv", SaveFormat.Csv);
                result.Save("table.xlsx", SaveFormat.Xlsx);
            }
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Error: {ex.Message}");
        }
    }
}

Use Cases and Applications

Financial Reports and Invoices

Extract transactional tables from images into Excel or database systems automatically.

Research and Analytics

Digitize tables from scanned publications or survey forms for data analysis.

Automated Data Migration

Migrate legacy documents or scanned paper records into modern structured formats.

Common Challenges and Solutions

Challenge 1: Blurry or Complex Table Images

Solution: Use clearer images or experiment with preprocessing to improve structure recognition.

Challenge 2: Non-Standard Table Layouts

Solution: Test and adjust settings for complex layouts or borderless tables.

Challenge 3: Large Batches or Mixed Image Types

Solution: Use batch processing and directory scanning to automate extraction from many files.

Performance Considerations

  • Use well-lit, high-res images
  • Batch process for efficiency
  • Dispose OCR objects after use

Best Practices

  1. Always validate exported table data before further processing
  2. Preprocess images for optimal structure detection
  3. Secure and backup original scans/images
  4. Use the right export format for your workflow (CSV, XLSX, JSON)

Advanced Scenarios

Scenario 1: Mixed-Language Table Extraction 

settings.Language = Language.Chinese;

Scenario 2: Combining Table and Text Extraction 

settings.DetectAreasMode = DetectAreasMode.COMBINE;

Conclusion

Aspose.OCR Table to Text for .NET transforms image tables into structured, editable data—no manual entry required. Speed up financial reporting, analytics, and digital archiving with accurate, automated table extraction.

For more examples and technical details, visit the Aspose.OCR for .NET API Reference .

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