Plugin.Xamarin.OCR 1.0.7

Plugin.Xamarin.OCR | Plugin.Maui.OCR

Plugin.Xamarin.OCR and Plugin.Maui.OCR provide the ability to do simple text from image OCR using nothing but platform APIs.

Should you use this yet?

YES. Let me know how it works for you.

What Works Matrix

Why

Why am I making this? I'm doing this because I want to make it easier for developers to do OCR in their apps. I want to make it so that you can just use this plugin and not have to worry about the platform specifics.

Too many times I've tried to do OCR and had to wrestle with external dependencies like Tesseract (with its dependencies Leptonica, etc) and these types of native dependencies can be a real pain to work with.

Xamarin??

Well, I still have to maintain a Xamarin app that uses Tesseract and I'm tired of all the problems that come with it. I want to make it easier for myself and others to do OCR in their apps.

Install Plugin

Available on NuGet for MAUI and Xamarin.

Install with the dotnet CLI: dotnet add package Plugin.Maui.OCR or dotnet add package Plugin.Xamarin.OCR, or through the NuGet Package Manager in Visual Studio.

Supported Platforms

MAUI Setup and Usage

For MAUI, to initialize make sure you use the MauiAppBuilder extension AddOcr() like so:

public static class MauiProgram
{
	public static MauiApp CreateMauiApp()
	{
		var builder = MauiApp.CreateBuilder();
		builder
			.UseMauiApp<App>()
			.ConfigureFonts(fonts =>
			{
				fonts.AddFont("OpenSans-Regular.ttf", "OpenSansRegular");
				fonts.AddFont("OpenSans-Semibold.ttf", "OpenSansSemibold");
			}).
			AddOcr();  // <-- add this line

		return builder.Build();
	}
}

And then you can just inject IOcrService into your classes and use it like so:

/// <summary>
/// Takes a photo and processes it using the OCR service.
/// </summary>
/// <param name="photo">The photo to process.</param>
/// <returns>The OCR result.</returns>
private async Task<OcrResult> ProcessPhoto(FileResult photo)
{
    // Open a stream to the photo
    using var sourceStream = await photo.OpenReadAsync();

    // Create a byte array to hold the image data
    var imageData = new byte[sourceStream.Length];

    // Read the stream into the byte array
    await sourceStream.ReadAsync(imageData);

    // Process the image data using the OCR service
    return await _ocr.RecognizeTextAsync(imageData);
}

Xamarin Setup and Usage

WIP

Details

The IOcrService interface exposes the following methods:

public interface IOcrService
{
    Task InitAsync(CancellationToken ct = default);
    Task<OcrResult> RecognizeTextAsync(byte[] imageData, bool tryHard = false, CancellationToken ct = default);
}

public class OcrResult
{
    public bool Success { get; set; }

    public string AllText { get; set; }

    public IList<OcrElement> Elements { get; set; } = new List<OcrElement>();
    public IList<string> Lines { get; set; } = new List<string>();

    public class OcrElement
    {
        public string Text { get; set; }
        public float Confidence { get; set; }

        // Useful for bounding boxes
        public int X { get; set; }
        public int Y { get; set; }
        public int Height { get; set; }
        public int Width { get; set; }
    }
}

Permissions

Before you can start using Feature, you will need to request the proper permissions on each platform.

iOS

If you're handling camera, you'll need the usual permissions for that.

Android

If you're handling camera, you'll need the usual permissions for that. The only extra part you'll want in the AndroidManifest.xml is the following:

<application ..>
  <meta-data android:name="com.google.mlkit.vision.DEPENDENCIES" android:value="ocr" />
</application>

This will cause the model necessary to be installed when the application is installed.

Dependency Injection

You will first need to register the OcrPlugin with the MauiAppBuilder following the same pattern that the .NET MAUI Essentials libraries follow.

builder.Services.AddSingleton(OcrPlugin.Default);

You can then enable your classes to depend on IOcrService as per the following example.

public class OcrViewModel
{
    readonly IOcrService _ocr;

    public OcrViewModel(IOcrService ocr)
    {
        _ocr = ocr;
    }

    public void DoSomeOcr()
    {
        byte[] imageData = GetImageData();

        var result = await _ocr.RecognizeTextAsync(imageData);
    }
}

Straight usage

Alternatively if you want to skip using the dependency injection approach you can use the Feature.Default property.

public class OcrViewModel
{
    public void DoSomeOcr()
    {
        byte[] imageData = GetImageData();

        var result = await OcrPlugin.Default.RecognizeTextAsync(imageData);
    }
}

Feature

Once you have the OCR instance, you can interact with it in the following ways:

Events

RecognitionCompleted

This event is fired when the OCR service has completed recognizing text from an image. The event args contain the OcrResult object. Only fires if the StartRecognizeTextAsync method is called.

Properties

SupportedLanguages

A list of supported languages for the OCR service. This is populated after calling InitAsync. Allows you to know what language codes are able to be used in OcrOptions.

Methods

InitAsync(CancellationToken ct = default)

Initialize the feature. If supported on the platform (like iOS), SupportedLanguages will be populated with the available languages.

RecognizeTextAsync(byte[] imageData, bool tryHard = false, CancellationToken ct = default)

Recognize text from an image. Specify "tryHard" if you want to tell the platform API to do a better job (fast vs accurate, and use language correction (ios/mac)) though it seems very accurate normally.

RecognizeTextAsync(byte[] imageData, OcrOptions options, CancellationToken ct = default)

Recognize text from an image. OcrOptions contains options for the OCR service, including the language to use and whether to try hard.

Task StartRecognizeTextAsync(byte[] imageData, OcrOptions options, CancellationToken ct = default)

Start recognizing text from an image. This is a task that will fire the RecognitionCompleted event when it completes with the result.

Acknowledgements

This project could not have came to be without these projects and people, thank you! ❤️