React Native MLKit vs Accessibility Techniques Usage & Stats

Bring the advanced machine learning capabilities of MLKit to your React Native applications with our comprehensive wrapper. Our library makes it simple to implement powerful features like Document Scanning, Face Detection, Image Labeling, and Object Detection, all optimized for real-time on-device processing. Key Features: - Face Detection: Detect and analyze faces in images and videos in real-time. - Image Labeling: Automatically identify and label objects within images. - Object Detection: Recognize and track objects in real-time for dynamic interactions. Why Choose Our MLKit Wrapper: - Optimized Performance: Enjoy fast and efficient on-device processing. - Easy Integration: Simple and intuitive APIs for quick implementation. - Offline Capability: Utilize ML features without needing an internet connection. - Enhance your app's functionality with the powerful machine learning tools, tailored specifically for React Native developers. Download now and take your mobile applications to the next level! NOTE: None of the data is collected (i.e. faces). No collected images will leave your device and all image processing happens on device.

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Accessibility Techniques

iOS SwiftUI sample code demonstrating a variety of good and bad accessibility coding techniques. Demonstrates accessibility techniques for iOS SwiftUI apps using a collection of good and bad examples that can be tested with VoiceOver and other iOS assistive technologies. Using the app with VoiceOver will demonstrate the right and wrong ways of applying accessibility to a SwiftUI app. Review the project source code to see how to apply the accessibility techniques in working SwiftUI code examples. Find the project on GitHub at https://github.com/cvs-health/ios-swiftui-accessibility-techniques Contribute code or leave issues and suggestions on GitHub.