Text Recognition, Text Scanner, Handwriting Text Recognition OCR

Abstract

This project focuses on the development of a native iOS application for Optical Character Recognition (OCR) using Apple’s Vision Framework. The app is designed to detect, extract, and digitize both printed and handwritten text from various image sources, providing users with a fast, accurate, and convenient tool for tasks such as document scanning, note-taking, record keeping, and content sharing. The application supports multiple input options: users can scan text directly using the device camera, select images from the photo library, or upload documents through a file picker. Once an image is selected, the app performs real-time text recognition, highlighting detected regions with bounding boxes and displaying interactive previews. Recognized text can be easily copied, saved as a .txt file, or shared through native system integrations. The user interface is carefully designed to ensure clarity, responsiveness, and accessibility, including Voice Over support for visually impaired users. It is fully optimized for various iPhone screen sizes. To enhance global usability, the app is localized into over 10 languages, including English, Hindi, Chinese, Japanese, Arabic, French, German, and Spanish. This ensures a seamless experience for a wide range of users across different regions. Importantly, the application operates fully offline, prioritizing user privacy and data security by eliminating the need for internet connectivity or external servers. Its combination of multilingual support, offline capability, and a lightweight yet powerful interface makes it an ideal tool for students, researchers, and professionals alike.