How to Read Handwriting Images with IronOCR

What Input Format Should I Use?

:path=/static-assets/ocr/content-code-examples/how-to/read-handwritten-image.cs

using IronOcr;

// Instantiate OCR engine
var ocr = new IronTesseract();

// Load handwriting image
var inputHandWriting = new OcrInput();
inputHandWriting.LoadImage("handwritten.png");

// Perform OCR on the handwriting image
OcrHandwritingResult result = ocr.ReadHandwriting(inputHandWriting);

// Output the recognized handwritten text
Console.WriteLine(result.Text);
// Output the confidence score of the OCR result
Console.WriteLine(result.Confidence);

What Results Can I Expect?

The ReadHandwriting method achieved a 90.6% confidence score, correctly identifying the majority of text including the opening phrase "My name is Erin Fish."

This strong result demonstrates IronOCR's capability with challenging handwritten script. While the engine struggled with spacing and connected letters, it successfully extracted the core message. This shows IronOCR handles complex, non-standard text effectively.

For those new to OCR, start with our simple OCR tutorial to understand basics before tackling handwriting recognition.

How Do I Use the Async Version?

IronOCR supports an asynchronous version: ReadHandwritingAsync. This is useful when dealing with asynchronous code that requires fetching input images before processing. The async support documentation provides comprehensive guidance on implementing asynchronous OCR operations.

Using the same input, here's how to use the async method:

:path=/static-assets/ocr/content-code-examples/how-to/read-handwritten-image-async.cs

using IronOcr;
using System.Threading.Tasks;

// Instantiate OCR engine
var ocr = new IronTesseract();

// Load handwriting image
var inputHandWriting = new OcrInput();
inputHandWriting.LoadImage("handwritten.png");

// Perform OCR using the async method with 'await'.
// The compiler automatically infers this top-level code block as an 'async Task Main()' method.
OcrHandwritingResult result = await ocr.ReadHandwritingAsync(inputHandWriting);

// Output the recognized handwriting text
Console.WriteLine(result.Text);
// Output the confidence score of the OCR result
Console.WriteLine(result.Confidence);

You can provide an optional timeoutMs parameter to specify milliseconds before automatic cancellation. The default value is -1, meaning no time limit—the operation runs until completion.

Advanced Processing Techniques

For complex handwriting recognition scenarios, consider these advanced techniques:

Region-Specific OCR: When working with forms or structured documents, use region-based OCR to focus on specific areas containing handwritten text. This approach improves accuracy by limiting the processing area:

using IronOcr;
using IronSoftware.Drawing;

var ocrTesseract = new IronTesseract();
using var ocrInput = new OcrInput();

// Define a specific region for signature area
var signatureRegion = new CropRectangle(x: 100, y: 500, width: 300, height: 100);
ocrInput.LoadImage("form-with-signature.png", signatureRegion);

var signatureResult = ocrTesseract.ReadHandwriting(ocrInput);
Console.WriteLine($"Signature text: {signatureResult.Text}");

using IronOcr;
using IronSoftware.Drawing;

var ocrTesseract = new IronTesseract();
using var ocrInput = new OcrInput();

// Define a specific region for signature area
var signatureRegion = new CropRectangle(x: 100, y: 500, width: 300, height: 100);
ocrInput.LoadImage("form-with-signature.png", signatureRegion);

var signatureResult = ocrTesseract.ReadHandwriting(ocrInput);
Console.WriteLine($"Signature text: {signatureResult.Text}");

Progress Tracking: For batch processing of multiple handwritten documents, implement progress tracking to monitor the OCR operation:

ocrTesseract.OcrProgress += (sender, e) => 
{
    Console.WriteLine($"Processing: {e.ProgressPercent}% complete");
};

ocrTesseract.OcrProgress += (sender, e) => 
{
    Console.WriteLine($"Processing: {e.ProgressPercent}% complete");
};

What Challenges Should I Be Aware Of?

Although IronOCR achieves high confidence in retaining overall structure and text, OCR still struggles with handwriting, leading to localized errors. Common challenges require verification of extracted output:

Irregular Spacing: Print text has uniform spacing between letters. Handwriting spacing between strokes and letter connections varies greatly. This causes incorrect character segmentation, as shown when ununiformed splits into individual characters (u n u n i f o c m e d) instead of a single word.

Stroke Variation: Each person has unique handwriting, and individuals write the same letter differently each time. Letter connections and patterns vary significantly. This prevents a "one-size-fits-all" model, as the engine must handle high variability in stroke slant, pressure, and form, making pattern matching less reliable than with standardized fonts.

Ambiguous Character Shapes: Handwriting often uses simplified or hurried strokes, creating ambiguous shapes. A quickly written e might resemble a c, or connected l and i might be misidentified.

Quality and Resolution Issues: Poor scan quality, low resolution, or faded ink significantly impact recognition accuracy. When encountering such issues, refer to our general troubleshooting guide for solutions.

When using this method, verify output matches intended input, paying special attention to closely spaced or poorly formed words. Consider implementing post-processing logic to handle common misrecognitions specific to your use case.