How to use Custom Language with Tesseract in C#

What Training Data Do I Need for Custom Languages?

We'll use this sample PDF, which contains text in our custom language, as the input.

We'll be using this custom language .traindata for our example.

The quality and comprehensiveness of your training data directly impacts OCR accuracy. When preparing custom language training data:

1. Character Coverage: Ensure your training data includes all characters and symbols
2. Font Variations: Include multiple font styles if your documents vary in typography
3. Image Quality: Train with images similar to those you'll process in production
4. Context Patterns: Include common word combinations and phrases

For advanced configuration options, see our Tesseract detailed configuration guide.

How Do I Load and Process Custom Language Documents?

:path=/static-assets/ocr/content-code-examples/how-to/ocr-custom-language.cs

using IronOcr;
using System;
using System.IO;

var ocrTesseract = new IronTesseract();

// Load the traineddata file for the custom language
ocrTesseract.UseCustomTesseractLanguageFile("AMGDT.traineddata");

using var ocrInput = new OcrInput();
// Load the PDF containing text in the custom language
ocrInput.LoadPdf("custom.pdf");

var ocrResult = ocrTesseract.Read(ocrInput);

// Print text to the console
Console.WriteLine("--- OCR Result ---");
Console.WriteLine(ocrResult.Text);
Console.WriteLine("------------------");

// Pipe text to a .txt file
string outputFilePath = "ocr_output.txt";
File.WriteAllText(outputFilePath, ocrResult.Text);

Console.WriteLine($"\nSuccessfully saved text to {outputFilePath}");

The above code demonstrates the basic workflow for custom language OCR. For more complex scenarios, consider these enhancements:

Optimize Performance: For large documents or batch processing, implement multithreading and async support to improve performance.

Image Preprocessing: If your source documents have quality issues, apply image correction filters before OCR processing. The Filter Wizard can help you find the optimal preprocessing settings.

Region-Specific OCR: For documents with mixed content, use the OCR region of an image technique to focus on specific areas containing your custom language.

What Results Can I Expect from Custom Language OCR?

This output shows the result from our custom language model. By providing the correct training data, IronOCR successfully deciphered the text, and the result is in plain English. Additionally, this is the txt output generated by the code.

The accuracy of custom language OCR depends on several factors:

• Training Data Quality: Better training data yields better results
• Document Consistency: Documents matching the training data perform best
• Image Resolution: Higher DPI images produce more accurate results - see our guide on DPI settings

Best Practices for Custom Language Implementation

When implementing custom language OCR in production environments, consider these best practices:

Error Handling and Validation: Always validate that your .traineddata file exists and is accessible before attempting to load it. Implement proper error handling for cases where the custom language file might be missing or corrupted.

Performance Optimization: Custom language models can be larger than standard language packs. For optimal performance:

• Cache the loaded language model when processing multiple documents
• Use progress tracking to monitor long-running OCR operations
• Consider implementing timeouts for processing large documents

Combining with Standard Languages: If your documents contain both custom and standard languages, you can load multiple languages simultaneously. This is particularly useful for documents with mixed content.

Testing and Validation: Establish a testing framework to validate OCR accuracy:

• Create a test dataset with known outputs
• Use result confidence metrics to assess recognition quality
• Implement highlight texts as images for visual debugging

Advanced Use Cases

Custom language OCR opens up numerous possibilities:

Historical Document Preservation: Digitize ancient manuscripts or texts written in obsolete scripts
Specialized Notation Systems: Process mathematical equations, musical notation, or technical diagrams - see our equations troubleshooting guide
Security Applications: Decode proprietary encoding systems or ciphers
Accessibility: Convert specialized braille or tactile writing systems to standard text

For more advanced scenarios, explore our comprehensive code examples showcasing various IronOCR capabilities with Tesseract 5.