ScanLingo: Learn by Reading · Reading learning guides
How to turn photo object candidates into vocabulary cards
Updated 2026-08-29
Treat general-object candidates as prompts, then check and edit them before saving local vocabulary cards.
Capture and check the original
Object vocabulary uses the bundled OpenMMLab RTMDet-s FP16 Core ML model on the device to generate general-object candidates from an image you choose or take; you may review, edit, and save vocabulary. The model is not downloaded at runtime, and the current release does not send source images to the operator's servers. The bundled model file is approximately 20.2 MB.
Camera and photo access are requested only when you choose the related feature, and you can change access at any time in iOS Settings.
Use language tools to clarify meaning
Machine recognition, translation, and speech output may be incomplete or inaccurate and must not replace human review in professional or high-risk contexts.
Lighting, clarity, layout, and language affect OCR accuracy. Check the result against the original before relying on it.
Keep a route back to the source
Bind recognised material to a book and page number, organise it as sentence cards, and return to that context while reviewing local learning progress and reports.
Book and page associations, sentence cards, learning profiles, and reports are stored on your device by default. The current release has no remote account or sync feature.
Understand accuracy and local-storage boundaries
Images, OCR text, translations, and learning content are processed and stored on the device by default. Speech-input results are stored on the device by default; the operator does not retain microphone audio.
The current release has no advertising and no analytics. It also does not include advertising tracking, cross-app tracking, IDFA use, heat maps, or session replay.