ScanSpeak � A Smart Document Digitization Application

Abstract

Access to reliable and convenient methods of converting physical information into digital formats remains a challenge for students and professionals who regularly need to preserve, edit, and share written or spoken content. This project proposes the development of ScanSpeak, a mobile application built using Flutter and Dart, designed to convert hardcopy documents and spoken words into editable softcopy formats. The application combines Optical Character Recognition (OCR) for scanning printed or handwritten text and Speech-to-Text (STT) technology for transcribing verbal input, offering users a unified, offline-capable tool for digitizing information from multiple sources.

Objectives

To design and develop a mobile application capable of extracting text from hardcopy documents using OCR technology.
To implement a speech-to-text module that converts spoken words into editable text in real time.
To provide document management features, including editing, saving, categorizing, and exporting digitized content in multiple formats (.txt, .docx, .pdf).
To incorporate smart features such as automated text correction, document classification, and translation to improve usability and accuracy.
To evaluate the application's performance in terms of accuracy, usability, and processing speed under real-world conditions.

Methodology
The application will be developed using the Flutter framework with Dart as the programming language, enabling cross-platform mobile deployment. OCR functionality will be implemented using on-device machine learning libraries (e.g., Google ML Kit Text Recognition), while speech recognition will use platform-native speech-to-text APIs to support offline functionality and protect user privacy. The development will follow an iterative, agile-based approach consisting of requirements gathering, system design, incremental implementation, and continuous testing. Local storage will be handled using a lightweight embedded database (e.g., SQLite/Hive) for document history and search functionality. User testing will be conducted to evaluate OCR/STT accuracy across varying lighting conditions, document types, and speech patterns.

Expected Outcomes
The project is expected to produce a functional, user-friendly mobile application that allows users to convert both physical documents and spoken language into accurate, editable digital text without reliance on internet connectivity. It is anticipated that the application will improve efficiency in digitizing academic and personal documents, reduce manual transcription effort, and demonstrate practical integration of computer vision and natural language processing techniques within a mobile environment. The application will also serve as a proof-of-concept for offline, privacy-preserving document digitization solutions suitable for use in low-connectivity environments such as those found in parts of Zambia.