Android OCR Libraries (and Where .NET Developers Fit In)

If you're adding text recognition to an Android app, you have a handful of OCR libraries to pick from, and they don't all solve the same problem. Some run on-device, some call a cloud API, and one of them is technically a .NET library that reaches Android through .NET MAUI. We'll walk through the main options, show some code, and be honest about the trade-offs.

Upfront: we work on IronOCR at Iron Software. It's a .NET library, not a native Android SDK. We'll be clear about where it fits (cross-platform code through .NET MAUI, which can target Android) versus the Android-native options below, so you can tell which tool matches your stack.

OCR libraries take an image, find the text inside it, and hand it back as a string you can search, store, or translate. On Android that powers document scanning, live translation, receipt capture, and form data entry. The libraries differ mostly in where the work happens (on the phone or in the cloud), how many languages they cover, and how much setup they ask of you.

The Android OCR options

Tesseract OCR

Tesseract is the open-source OCR engine most people start with. It supports over 100 languages and runs offline. The engine itself is C++, so on Android you reach it through a wrapper rather than calling it directly. For years that wrapper was tess-two, though it's no longer maintained. It's a solid choice when you want local processing and no per-request cost.

Google Mobile Vision API

The Mobile Vision API used to be the quick on-device option bundled with Google Play services. It's deprecated now and you should not start new projects with it. Google's replacement is ML Kit (covered below), which has the current text-recognition models and ongoing support.

Microsoft Azure Computer Vision

Azure's vision service does OCR in the cloud alongside image tagging, object detection, and other analysis. It handles many languages and reads tricky images well, but it needs a network connection and you pay per call. It's a good fit when your app is already online and you want accuracy without shipping models to the device.

ABBYY Mobile Web Capture

ABBYY's Mobile Web Capture is a JavaScript SDK aimed at document capture inside web-based flows, like onboarding screens. A user points the camera at a document and the SDK captures a clean image for processing. It's commercial and built more for capture-and-submit pipelines than for general in-app OCR.

ML Kit

ML Kit is Google's current on-device option and the migration path away from Mobile Vision. Text recognition runs locally, so it's fast and keeps image data on the phone. You get a clean API without needing to train or host models yourself. For most new Android-native apps that want offline OCR, this is the default we'd reach for first.

Tesseract4Android

Tesseract4Android is a fork of tess-two, rewritten to work with CMake and recent Android Studio versions. It wraps the Tesseract OCR engine with Java and JNI, so you get Tesseract's accuracy and language coverage with an interface that fits a modern Android project. Since tess-two is abandoned, this is the maintained way to run Tesseract on Android today.

It bundles Tesseract 5.3.4, Leptonica 1.83.1 for image processing, and libjpeg/libpng for image handling. Setup is two Gradle edits and then a few lines of code.

First, add the JitPack repository to your root build.gradle:

allprojects {
    repositories {
        ...
        maven { url 'https://jitpack.io' }
    }
}

Then add the dependency in your app module's build.gradle. Pick the standard or OpenMP variant depending on whether you want the extra threading:

dependencies {
    // Standard variant
    implementation 'cz.adaptech.tesseract4android:tesseract4android:4.7.0'
    // OpenMP variant
    implementation 'cz.adaptech.tesseract4android:tesseract4android-openmp:4.7.0'
}

With that in place, you talk to TessBaseAPI: point it at your language data and an image, then read the text back. Here's a small manager class that wraps the lifecycle:

import com.googlecode.tesseract.android.TessBaseAPI;
import android.graphics.Bitmap;

public class OCRManager {
    private TessBaseAPI tessBaseAPI;

    public OCRManager(String dataPath, String language) {
        tessBaseAPI = new TessBaseAPI();
        tessBaseAPI.init(dataPath, language);
    }

    public String recognizeText(Bitmap bitmap) {
        tessBaseAPI.setImage(bitmap);
        return tessBaseAPI.getUTF8Text();
    }

    public void onDestroy() {
        if (tessBaseAPI != null) {
            tessBaseAPI.end();
        }
    }
}

Where IronOCR fits for .NET teams

Everything above is for native Android development in Java or Kotlin. If your team writes C# instead, you're in a different lane. IronOCR is a .NET OCR library, not an Android SDK, so it won't drop into a Kotlin project. Where it can reach Android is through .NET MAUI: you write one C# codebase and MAUI builds it for Android (along with iOS, Windows, and macOS).

So the honest framing is this. If you're building a native Android app, one of the libraries above is the right call, and ML Kit or Tesseract4Android are the two we'd shortlist. If you're already a .NET shop and want OCR in cross-platform C# that happens to run on Android, IronOCR is worth a look. It reads text from images and scanned documents, supports many languages, and works across .NET targets including .NET Core, .NET Framework, and MAUI.

To add it to a .NET project, install the NuGet package:

Install-Package IronOcr

Then the API is a couple of lines. Point IronTesseract at an image and read the .Text off the result:

using IronOcr;

class Program
{
    static void Main(string[] args)
    {
        string imageText = new IronTesseract().Read(@"images\image.png").Text;
        Console.WriteLine("Recognized Text:");
        Console.WriteLine(imageText);
    }
}

The same call works whether you run it on a desktop service or inside a MAUI app targeting Android, since you're using the same C# across platforms.

Picking one

Here's the same set of options side by side, so you can match a tool to your constraints at a glance:

There's no single best answer here, only the one that matches your codebase and where the work needs to happen:

• Native Android, offline: ML Kit for the current Google path, or Tesseract4Android if you want Tesseract's language coverage.
• Native Android, cloud-backed accuracy: Azure Computer Vision.
• Capture inside web flows: ABBYY Mobile Web Capture.
• C# / cross-platform via MAUI: IronOCR. There's a free trial if you want to test it against your own images before committing.

And skip Mobile Vision for anything new, since it's deprecated.

What are you building OCR into, and which way did you lean? If you've shipped one of these on Android, we'd like to hear how accuracy and setup held up in production. Drop a comment with your experience.