# ML Kit Published articles for ML Kit. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building Menu Vision: Real-Time Dish Recognition DevFeed: [Building Menu Vision: Real-Time Dish Recognition]() Original publisher: [Read original article]() Author: Arpitha Dudi, Growth Tech Lead Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Computer vision](), [Artificial Intelligence](), [Android](), [cameraX](), [ML Kit](), [Hackathon](), [Development](), [iOS]() Tags: [ai](), [android](), [camerax](), [data-pipeline](), [development](), [hackathon](), [ios](), [ml-kit](), [pipeline](), [prototype](), [real-time](), [recognition](), [vision]() ### AI overview Yelp describes Menu Vision, a feature that uses a phone camera, AI, augmented reality, and text recognition to identify dishes on restaurant menus and surface related user photos and reviews. The article covers its hackathon-origin Android prototype and the production system's on-device machine learning, real-time computer vision, fuzzy matching, and distributed data pipeline. ### Source excerpt Menus aren't just lists, they're a window into a restaurant's unique offerings, specialties, and personality, shaping where and what we choose to eat. But here's the challenge: reading "Kung Pao Chicken - Stir-fried chicken with peanuts in spicy sauce" doesn't tell you what the portions look like, whether other diners loved it, or if it matches your expectations. At Yelp, we knew we had the solution sitting in our user-generated content: hundreds of millions of photos, reviews, and prices for dishes. The problem? Users had to manually search for each dish, an experience that doesn't work well when you're at... ## Scanning bus stop codes with ML Kit and Vision in the GalwayBus Compose Multiplatform app DevFeed: [Scanning bus stop codes with ML Kit and Vision in the GalwayBus Compose Multiplatform app]() Original publisher: [Read original article]() Published: 2026-08-06T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [compose-multiplatform](), [ML Kit](), [Android](), [cameraX](), [iOS](), [Code](), [ui]() Tags: [android](), [apple](), [camera](), [camerax](), [code](), [compose-multiplatform](), [ios](), [ml-kit](), [multiplatform](), [native](), [ocr](), [on-device](), [platform](), [recognition](), [ui]() ### AI overview This tutorial explains how the GalwayBus app scans six-digit bus stop codes using on-device ML Kit on Android and Apple's Vision framework on iOS. Camera preview, matching logic, and UI are shared through Compose Multiplatform, while platform-specific implementations handle camera access and OCR. Recognized six-digit runs are matched exactly to stops, and overlapping recognition requests are avoided by dropping newer frames. ### Source excerpt Every bus stop in Galway has a plate with a 6-digit stop code printed on it. We recently added a "Scan" tab to the GalwayBus app that lets you point the camera at that plate and jump straight to the stop's departures. The text recognition runs entirely on device (ML Kit on Android, Apple's Vision framework on iOS), with the camera preview, matching logic and UI all living in the shared Compose Multiplatform code. ## Build intelligent Android apps: On-device inference DevFeed: [Build intelligent Android apps: On-device inference]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-21T13:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [Android](), [On-device AI](), [ML Kit](), [Inference](), [gemma4](), [Google](), [Mobile]() Tags: [android](), [efficiency](), [features](), [gemini](), [gemma](), [gemma-4](), [google](), [inference](), [ml-kit](), [on-device](), [speed]() ### AI overview This Android developer article explains how to use Gemini Nano through ML Kit's Prompt API to build on-device features in the Jetpacker demo app. It covers itinerary summarization, expense management, and voice notes, emphasizing local processing, privacy, offline reliability, and avoiding cloud inference costs. The article reports that prompt iteration reduced response time from 13 seconds to under 2 seconds. ### Source excerpt Posted by Caren Chang, Developer Relations Engineer, Android Developer Relations Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we introduced Jetpacker, the demo app we'll use throughout this series. In this blog post, we will share how you can use Gemini Nano through ML Kit's Prompt API to build intelligent on-device features. Building intelligent on-device features refers to the ability to process prompts and data directly on a device without sending data to a server. This offers a few advantages: User data can be processed locally on the device, preserving user privacy Functionality of the model is reliable even with spotty or no internet connection No additional cloud inference cost, since everything runs on the user's hardware With the benefits of on-device in mind, we identified three features to add in Jetpacker that can improve the user experience: summarizing trip itineraries, managing expenses, and capturing voice notes. On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes High quality tailored summarization of short texts The itinerary screen gives users a quick overview of all activities for a given trip. Since this screen contains a lot of information, it can quickly become overwhelming. To help users prepare without feeling overwhelmed, we can add a 'Get ready for your trip' section at the top. The romantic Paris trip is summarized as a classic Parisian adventure blending art, sights, and delicious food. A tip and some useful phrases are also added. By inputting a trip itinerary and asking an LLM to summarize it, we can generate a quick summary of the trip along with packing tips and useful local phrases. This is a great use case for an on-device model for several reasons: Performance and quality: Both the input and output text are relatively short. With tha ## ML Kit Document Scanner in action DevFeed: [ML Kit Document Scanner in action]() Original publisher: [Read original article]() Author: Julien Salvi Published: 2024-04-15T09:53:27Z Content type: tutorial Language: en Sources: [Stories by Julien Salvi on Medium]() Topics: [ML Kit](), [Android](), [API](), [pdf](), [Google]() Tags: [android](), [android-development](), [api](), [code](), [gradle](), [kotlin](), [machine-learning](), [ml-kit](), [ml-kit-android](), [pdf](), [scanner]() ### AI overview This tutorial explores ML Kit Document Scanner for Android and demonstrates integrating it into a messaging feature. The library digitizes physical documents, supports automatic capture, detection, cropping, rotation, image editing, and optional gallery import, with the scan flow delivered through Play Services. ### Source excerpt Recently, ML Kit released a new library to digitize physical documents directly from your Android application. ML Kit Document Scanner allows the users to easily scan documents with maximum controls and minimum code integration. During my time at Aircall, I took some time to explore and integrate the library to build an Android-exclusive feature for the messaging scope. Discovering the library ML Document Scanner is the newborn of the ML Kit family. It allows users to convert physical documents into digital formats. The library now brings this feature to any apps with many capabilities like applying filters, cleaning the image, removing shadows and many more. The entire scan flow is delivered by the Play Services so no camera permission is mandatory and the library size (~300KB download size increase) has a low impact in your app (the ML models are also managed by the Play Services). So let's dig into the what the library offers. The ML Kit Document Scanner offers key capabilities like a high-quality user interface that ensures consistency across Android applications. With automatic capture and document detection, the users can effortlessly scan documents, while accurate edge detection ensures optimal cropping results. Additionally, automatic rotation detection ensures that scanned documents are presented upright. For developers seeking customization options, the Document Scanner API offers flexibility. You can set a limit on the number of pages scanned and enable or disable the capability to import from the photo gallery. Moreover, there are 3 editing modes available: basic editing capabilities, editing with image filters or editing with ML-enabled image cleaning capabilities (erase stains, fingers...). You will find a complete overview of the library with the official documentation. ML Kit | Google for Developers Build with Document ScannerCrafting the feature It's now time to craft our new feature with ML Kit Document Scanner. Here, we want to give the ability to t ## Product news and other highlights from Firebase Live DevFeed: [Product news and other highlights from Firebase Live]() Original publisher: [Read original article]() Author: Francis Ma Published: 2020-07-22T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [code productivity](), [Command-line interface](), [Web app]() Tags: [authentication](), [emulator-suite](), [extensions](), [firebase](), [google-analytics](), [ml-kit](), [news](), [product-news](), [security-rules](), [tutorials](), [updates]() ### AI overview Firebase recaps its Firebase Live web series, including product updates, videos, codelabs, an interactive demo, and answers to developer questions. The article highlights the new local emulator UI, which supports local development, log filtering, data testing, and safer experimentation without affecting shared environments or incurring billing costs. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Enhance your TensorFlow Lite deployment with Firebase DevFeed: [Enhance your TensorFlow Lite deployment with Firebase]() Original publisher: [Read original article]() Author: Khanh LeViet Published: 2020-06-26T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [TensorFlow Lite](), [Deployment](), [Firebase ML](), [model-deployment](), [Mobile]() Tags: [android](), [deployment](), [firebase](), [firebase-ml](), [google](), [inference](), [ios](), [machine-learning](), [ml-kit](), [remote-config](), [tensorflow-lite]() ### AI overview This tutorial explains how to use Firebase Machine Learning to enhance TensorFlow Lite model deployment in Android and iOS applications. It covers over-the-air model delivery, downloading models on demand, model management through the Firebase Console or API, and production features such as inference-speed measurement and A/B testing. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Submit your ideas for the Android Developer Challenge DevFeed: [Submit your ideas for the Android Developer Challenge]() Original publisher: [Read original article]() Author: Laura Willis Published: 2019-11-20T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Android](), [ML Kit](), [Firebase](), [User Experience](), [App]() Tags: [android](), [community](), [developer](), [firebase](), [ml-kit](), [news](), [products](), [updates](), [user-experience]() ### AI overview The Firebase team invites developers to submit ideas for the Android Developer Challenge by December 2. The challenge focuses on helpful innovation powered by on-device machine learning, with ML Kit for Firebase cited as an example technology. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new from Firebase at Google I/O 2019 DevFeed: [What's new from Firebase at Google I/O 2019]() Original publisher: [Read original article]() Author: Francis Ma Published: 2019-05-07T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [ML Kit](), [API](), [Google](), [object-detection](), [Mobile]() Tags: [c-plus-plus](), [crashlytics](), [emulator-suite](), [fabric](), [firebase](), [firestore](), [google](), [google-analytics](), [google-i-o](), [launch](), [machine-learning](), [ml-kit](), [mobile](), [object-detection](), [on-device](), [performance-monitoring](), [test-lab](), [updates]() ### AI overview Firebase announces its biggest Google I/O 2019 updates, including new ML Kit beta capabilities for on-device translation, real-time object detection and tracking, and custom image classification with AutoML Vision Edge. ### Source excerpt News, tutorials, and updates from the Firebase team. ## ML Kit expands into NLP with Language Identification and Smart Reply DevFeed: [ML Kit expands into NLP with Language Identification and Smart Reply]() Original publisher: [Read original article]() Author: Christiaan Prins; Max Gubin Published: 2019-04-02T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Natural language processing](), [Firebase](), [SDKs](), [Android](), [iOS](), [TensorFlow Lite](), [Transformer]() Tags: [android](), [firebase](), [ios](), [kotlin](), [language](), [launch](), [machine-learning](), [ml-kit](), [news](), [nlp](), [sdk](), [tensorflow-lite](), [updates]() ### AI overview Firebase announces two new ML Kit features: Language Identification and Smart Reply. The APIs support natural language processing, run fully on-device, and are available in the latest ML Kit SDK for iOS and Android. ### Source excerpt News, tutorials, and updates from the Firebase team. ## See how ML Kit and ARKit play together DevFeed: [See how ML Kit and ARKit play together]() Original publisher: [Read original article]() Author: Ibrahim Ulukaya Published: 2018-12-17T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Firebase](), [iOS](), [Mobile](), [Cloud APIs](), [object-detection]() Tags: [android](), [api](), [apple](), [arkit](), [cloud-apis](), [firebase](), [image](), [ios](), [machine-learning](), [ml-kit]() ### AI overview This tutorial explains how to combine ML Kit and ARKit in an iOS project. It describes processing camera frames with ML Kit image labeling, using on-device results for responsiveness and cloud-based labeling for higher accuracy, then displaying the detected label in a 3D scene. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Firebase's 2018 Product Launches Recap DevFeed: [Firebase's 2018 Product Launches Recap]() Original publisher: [Read original article]() Author: David East Published: 2018-12-12T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [ML Kit](), [Machine learning]() Tags: [api](), [bigquery](), [cloud-functions](), [emulator-suite](), [firebase](), [firestore](), [in-app-messaging](), [machine-learning](), [ml-kit](), [performance-monitoring](), [predictions](), [stackdriver](), [test-lab](), [year-in-review]() ### AI overview Firebase recaps 13 product launches from 2018. The supplied excerpts cover ML Kit for Android and iOS, Firebase In-App Messaging, and new REST APIs for task automation. ### Source excerpt News, tutorials, and updates from the Firebase team. ## ML Kit adds face contours to create smarter visual apps DevFeed: [ML Kit adds face contours to create smarter visual apps]() Original publisher: [Read original article]() Author: Christiaan Prins Published: 2018-11-06T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Computer vision](), [facial landmarks](), [Firebase]() Tags: [android](), [computer-vision](), [firebase](), [image](), [ios](), [launch](), [machine-learning](), [ml-kit](), [realtime](), [tutorials]() ### AI overview ML Kit adds face contour detection for visual apps on Android and iOS. The feature detects more than 100 points outlining the face and features such as the eyes, nose, and mouth, with contour points updating in real time. The article also describes facial landmark detection and links to Android and iOS documentation. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new at Firebase Summit 2018 DevFeed: [What's new at Firebase Summit 2018]() Original publisher: [Read original article]() Author: Francis Ma Published: 2018-10-29T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Cloud Platform (GCP)](), [Google]() Tags: [a-b-testing](), [app-development](), [app-quality](), [bigquery](), [cloud-functions](), [cloud-messaging](), [cloud-platform](), [community](), [crashlytics](), [data-studio](), [dynamic-audiences](), [firebase](), [firebase-summit](), [firestore](), [google-analytics](), [hosting](), [ios](), [live-streaming](), [machine-learning](), [ml-kit](), [news](), [performance-monitoring](), [predictions](), [realtime-database](), [remote-config](), [summit](), [tensorflow-lite](), [test-lab](), [unity](), [updates](), [web]() ### AI overview Firebase announces updates at Firebase Summit 2018, including beta support for Firebase through Google Cloud Platform support packages. The article also highlights Firebase adoption and a Hotstar case study involving increased user engagement. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Introducing ML Kit for Firebase DevFeed: [Introducing ML Kit for Firebase]() Original publisher: [Read original article]() Author: Sachin Kotwani Published: 2018-05-10T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Machine learning](), [SDKs](), [Mobile](), [Android](), [iOS](), [Cloud APIs](), [Google]() Tags: [android](), [api](), [apis](), [cloud-apis](), [firebase](), [google](), [google-i-o](), [ios](), [launch](), [machine-learning](), [ml](), [ml-kit](), [mobile](), [news](), [updates]() ### AI overview Firebase introduces ML Kit in beta, an SDK that helps Android and iOS developers add machine-learning features without requiring extensive machine-learning expertise. It includes ready-to-use APIs for text recognition, face detection, barcode scanning, image labeling, and landmark recognition, with both on-device and cloud-based options. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new in Firebase at I/O 2018 DevFeed: [What's new in Firebase at I/O 2018]() Original publisher: [Read original article]() Author: Francis Ma Published: 2018-05-08T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [AI Development](), [Mobile](), [SDK](), [Android](), [iOS](), [TensorFlow Lite](), [Amazon Machine Learning]() Tags: [android](), [firebase](), [google-analytics](), [google-cloud](), [google-i-o](), [ios](), [machine-learning](), [ml-kit](), [news](), [on-device](), [performance-monitoring](), [tensorflow-lite](), [test-lab](), [updates]() ### AI overview Firebase announces improvements at I/O 2018, including the public beta of ML Kit, an SDK for adding machine learning features to Android and iOS apps. ML Kit provides ready-to-use APIs for text recognition, face detection, barcode scanning, image labeling, and landmark recognition, with on-device and cloud-based options. Developers can also use their own TensorFlow Lite models, while Firebase handles hosting and serving. The article also begins discussing improvements to Performance Monitoring. ### Source excerpt News, tutorials, and updates from the Firebase team.