DOI and ISBN citation converter

Yen, Pei Xuan (2025) DOI and ISBN citation converter. Final Year Project, UTAR.

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Abstract

This project developed a web-based citation generator to automate the accurate formatting of academic references, addressing a significant need among students and researchers who struggle with manual citation processes. The system supports both Digital Object Identifiers (DOIs) and International Standard Book Numbers (ISBNs), generating citations in multiple styles including APA, Harvard, and IEEE. The methodology adopted is an Iterative approach. The application employs a three-tier architecture consisting of a lightweight HTML/CSS/JavaScript front end, a high-performance Python backend built on FastAPI, and DuckDB for persistent metadata storage. The system fetches metadata from external APIs asynchronously, using CrossRef for DOIs and Open Library for ISBNs, and using Pydantic models to provide strict input validation. A key innovation involves using DuckDB as a local cache to minimise redundant API calls and significantly improve response times. Testing of the system demonstrated its reliability in generating accurate citations, managing invalid inputs with ease, and efficiently processing multiple references through its advanced bulk upload functionality. The project provides a practical and user-friendly solution that saves time while maintaining academic integrity by reducing formatting errors. Future developments may include additional citation styles, export features, and cloud-based deployment to enhance scalability. Keywords: citation, APA style, IEEE style, Harvard style, citation converter, web application Subject Area: PN172 – Literary Composition Techniques

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