How to integrate ML Kit in Android application Some other interesting features of this API are extraction of structured data, different type of orientations support and performance boost, because, it run on your device itself without any network connection. That’s why we have to limit ML Kit barcode scanning API to only support these two formats in our application. But, my client was only looking for support of Code 39 and QR code for now. ML Kit’s barcode scanning API also detect all of the formats automatically and it can also tell you about the scanned format, that you can use for any validation in your application. You can see complete list of supported Barcode formats here. For example, Codabar, Code 39, Code 93, EAN-8, EAN-13, QR code, PDF417, etc. That includes both linear and 2D formats. ML Kit’s barcode scanning API can read and scan almost dozen different type of barcodes. It also help you to generate smart reply suggestions in your chats and email applications. It can help you to identify and translate text from almost 50+ different languages. Natural Language APIs handle features related to language identification and translation. You can use any or all of these APIs with LIVE camera feed or simple bitmap or images. Vision APIs handle features related to barcode scanning, face and object detection, object tracking, image labeling etc. Google’s ML Kit provide implementation for two different set of APIs. Because, you might still want to use another service from Firebase and that article will definitely help you in doing so. By the way, integrating Firebase in an Android application is totally different topic which I have discussed here in detail. And it is a prerequisites for all application with or without Firebase online features implementation. Any application that want to use any service/product or library, that is part of Firebase, must create a Firebase project and also integrate Firebase core features or SDK in it. Because, It is not possible to use ML Kit for Firebase as a standalone SDK. There was also an other reason for this quick shift to new ML Kit. As per my understanding, I might be in one of those few people who started integrating Google ML Kit in android applications after 2-3 days of it’s release as a separate SDK. We both were eager, me and my client, to give it a try and replace ML Kit for Firebase with this library. But, later when Google introduced ML Kit as a standalone library or SDK on June 3rd, 2020. And it was initially known as ML Kit for Firebase. Also Google’s ML Kit was part of Firebase at the time when I kick-started my implementation. Therefore, I decided to give a try to a new library or SDK with some extra features and capabilities. Why Google’s ML Kit?Īlthough, I have worked with Zxing barcode library in past for some other clients. And most importantly, it brings Google’s machine learning knowledge and expertise to our mobile application. It is easy-to-use and can be integrated in any Android and iOS application in few simple steps. ML Kit is a standalone library from Google with on-device machine learning capabilities. In this article we will learn about Google ML Kit and how can we integrate in Android application. Recently one of my clients asked me to implement barcode scanning features in an Android application, which I successfully implemented using Google’s ML Kit.
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