Transferring Buckwalter Transcription to a Batch Mode: A method to Making it More Accessible
Year: 2025
Type: article
Abstract: This study addresses the challenges associated with the manual application of the Buckwalter Arabic Transcription System, a pivotal tool in computational linguistics for representing Arabic script using ASCII characters. Although the system ensures high fidelity and reversibility, its usability is hindered by its non-phonetic nature, steep learning curve, and reliance on manual referencing. To enhance accessibility and usability, this research introduces a web-based batch-mode interface that automates the Buckwalter transcription process. The tool allows users to input Arabic text and instantly receive standardized Buckwalter transliterations alongside International Phonetic Alphabet (IPA) representations. This dual-output approach supports a wide range of applications in linguistics, education, and natural language processing. The study explores the theoretical and linguistic foundations of the Buckwalter system, outlines its strengths and weaknesses, and analyzes its morphological implications. It further presents practical examples using real-world data, including excerpts from the Universal Declaration of Human Rights. The batch-mode website (ipabwat.com) streamlines the transcription of large Arabic texts, offering downloadable results in CSV format and an intuitive interface suited for both novice and expert users. By integrating automation with linguistic precision, the tool eliminates the need for manual chart referencing and reduces transcription errors, thus broadening the scope of Arabic text processing. Ultimately, this work aims to democratize access to Arabic computational tools, making the Buckwalter system more functional for researchers, developers, and learners across disciplines. It represents a critical step forward in enhancing the usability and reach of Arabic linguistic technologies.
Source: Forum for Linguistic Studies
Authors: Talal Musaed Alghizzi, Ibrahim Alluhaybi
Institution: Imam Mohammad ibn Saud Islamic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G5174915960
OpenAlex ID: W4412514304