ML Kit Integration for Mobile Apps: Custom & Ready-Made APIs

ML Kit for Mobile Apps: Integration and Optimization

We integrate Google ML Kit into mobile apps for text recognition, face detection, object detection, and other AI features. In practice, clients often come with a task like: 'We need a receipt scanner that works on Android and iOS with >95% accuracy.' Behind the seeming simplicity of ML Kit lie nuances that only surface in production—from image orientation to speed differences on budget devices. We've gathered real implementation experience so you don't step on the same rake.

Common Problems When Working with ML Kit

Ready-made APIs (Text Recognition v2, Face Detection, Barcode Scanning) work correctly if the input image requirements are met. Face Detection with FaceDetectorOptions.PerformanceMode.ACCURATE on Android returns results in 80–150 ms on a Pixel 6, but on budget devices with Snapdragon 680 it's already 400+ ms. Using FAST mode drops accuracy when the head is rotated more than 30°.

On iOS, MLKitFaceDetection via VisionImage(image:) loses image orientation if image.orientation is not explicitly set from UIImage.imageOrientation. No crash occurs—faces simply aren't detected when the phone is held horizontally.

With custom TFLite models via CustomImageLabeler, metadata packaging is crucial. Without TFLiteMetadataHelper, the model doesn't know input normalization—you must either add metadata via flatbuffers or specify normalization manually through CustomRemoteModel options.

Why On-Device Often Beats Cloud?

On-device works offline, is faster, and incurs no API call costs. Cloud is more accurate for complex cases (multilingual OCR, non-standard fonts). For most B2C apps, a hybrid scheme is optimal: on-device as the primary path, cloud as fallback when confidence is low. This way we achieved 94% accuracy on standard receipts without paying for cloud calls.

How We Implement ML Kit: Receipt Recognition Case Study

A case from practice: an app for scanning receipts. ML Kit Text Recognition v2 on-device gave 94% accuracy on standard cash receipts, but only 67% on faded thermal paper. We added preprocessing via CIFilter (increased contrast, binarization) before passing to VisionImage—accuracy rose to 89% without switching to Cloud API.

For Android, integration goes through BarcodeScanning.getClient() or TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS). Models are auto-downloaded via Play Services on first launch—this must be accounted for in UX: the first inference may take several seconds until the model is loaded. We use ModuleInstallClient for explicit preloading during onboarding.

For custom models—FirebaseModelDownloader with ModelDownloadType.LOCAL_MODEL_UPDATE_IN_BACKGROUND. The model updates in the background; the app uses the current version until the next launch.

How to Avoid Common Integration Mistakes?

• Always check image.orientation on iOS—otherwise face detection breaks when the phone orientation changes.
• On Android, use ModuleInstallClient to preload models and avoid first-inference delay.
• For custom TFLite models, always pack metadata via TFLiteMetadataHelper; otherwise normalization will be wrong.
• Test on 5-10 real devices from different manufacturers—inference speed can vary by 3x.
• Implement fallback logic: when on-device confidence is low, send the request to Cloud API (if the task is critical).

Supported ML Kit APIs

What's Included in ML Kit Integration

We deliver a complete package:

• Audit of current architecture and selection of optimal API (ready-made vs custom)
• Integration of ML Kit SDK with image preprocessing configuration
• Testing on 10+ devices (different OS versions, manufacturers)
• Integration and configuration documentation
• Team training on working with models
• 6-month warranty after delivery

Process and Timeline

Requirements audit → API selection (ready-made vs custom) → SDK integration → preprocessing setup → testing on target devices → production accuracy monitoring setup.

Integration of one ready-made API (e.g., Barcode Scanning or Face Detection) — 2–4 business days. Custom TFLite model with preprocessing and fallback logic — 1–2 weeks. Pricing is determined individually.

We have implemented ML Kit in 15+ projects, including fintech and retail, and guarantee accuracy of at least 90% at acceptance stage. Contact us to evaluate your project—we'll find the optimal solution for your needs.