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Mlkit

Mlkit is an open-source Kotlin toolkit of Android libraries for adding machine-learning vision features to applications, including text recognition, barcode scanning, image labeling, face and face-mesh detection, object and pose detection, and selfie segmentation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mlkit — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Mlkit when building an Android application and you want modular Kotlin dependencies for image-recognition or detection features rather than implementing those integrations from scratch.

Who it is for

  • Android developers building applications that need on-device-style vision analysis features through library modules.
  • Teams selecting specific recognition or detection capabilities to integrate through Gradle.
  • Developers exploring the sample app to see demonstrations of the toolkit's modules.

Documented uses

  • Recognize text in images.
  • Read barcode content from analyzed images.
  • Label general objects, places, animals, or products in images.
  • Detect faces and facial contour points, or detect face-mesh information.
  • Locate objects in images; the README states that its object-detection module detects up to five objects per image.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The README links to Maven Central and a downloadable demonstration APK.

  • The shared mlkit-common dependency is required; individual feature modules are added separately.
  • The compile SDK minimum varies by release: 33 for 2.0.0+, 34 for 2.1.0+, and 35 for 2.4.0+.
  • Version 2.x replaced the former camera-core module with the separately maintained CameraScan library.
  • Upgrading from 1.x may require adjusting imports and accounting for the separately maintained ViewfinderView library.
  • The repository describes custom TensorFlow Lite model support for developers who need to use their own models.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add an Mlkit feature module to an Android project

Configure Gradle dependencies for a selected Mlkit capability.

Before you start

  • An Android project using Gradle.
  • A compile SDK that meets the requirement for the Mlkit release line: 35 or higher for 2.4.0+, 34 or higher for 2.1.0+, and 33 or higher for 2.0.0+.

Steps

  1. Add Maven Central to the project repositories in build.gradle or settings.gradle.

  2. Add com.github.jenly1314.MLKit:mlkit-common:2.5.0 to the module dependencies.

  3. Add the corresponding optional 2.5.0 feature dependency, such as mlkit-text-recognition or mlkit-barcode-scanning.

Expected outcome

The Android module declares the shared Mlkit library and the selected feature library as Gradle dependencies.

Guide limitations

  • The README documents dependency configuration but does not provide a complete application implementation recipe here.
  • Version 2.x uses CameraScan as its camera foundation; camera setup details are referred to that library's documentation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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13 October 2026
Original website title
jenly1314/MLKit · Official homepage title · retained in its source language
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