Article: Mask-embedded transformer for English text recognition and correction Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.35 pp.1 - 17 Abstract: As the digital age moves quickly, automatic recognition and correction of English text has become a significant job in the field of natural language processing (NLP). Most traditional ways of correcting text use simple statistical models and manual procedures, which do not work well with complicated grammatical, spelling, and semantic mistakes. This paper suggests an English text recognition and correction framework called MT-Tec, which is based on the improved transformer model and the masked embedding technique. MT-Tec can find and fix spelling mistakes, grammar mistakes, and vocabulary mistakes through multilevel context modelling and accurate error correction mechanisms. The MT-Tec framework works very well with many kinds of text errors and text qualities, and it is especially good at handling low-quality text. In general, the MT-Tec framework can be quite helpful for automatic proofreading, revising text, and learning a new language. Inderscience Publishers - linking academia, business and industry through research

Title: Mask-embedded transformer for English text recognition and correction

Authors: Haiying Sang

Addresses: School of Foreign Languages, Heze University, Heze – 274000, China

Abstract: As the digital age moves quickly, automatic recognition and correction of English text has become a significant job in the field of natural language processing (NLP). Most traditional ways of correcting text use simple statistical models and manual procedures, which do not work well with complicated grammatical, spelling, and semantic mistakes. This paper suggests an English text recognition and correction framework called MT-Tec, which is based on the improved transformer model and the masked embedding technique. MT-Tec can find and fix spelling mistakes, grammar mistakes, and vocabulary mistakes through multilevel context modelling and accurate error correction mechanisms. The MT-Tec framework works very well with many kinds of text errors and text qualities, and it is especially good at handling low-quality text. In general, the MT-Tec framework can be quite helpful for automatic proofreading, revising text, and learning a new language.

Keywords: English text recognition and correction; improved transformer; masked embedding; natural language processing; NLP.

DOI: 10.1504/IJICT.2025.149045

International Journal of Information and Communication Technology, 2025 Vol.26 No.35, pp.1 - 17

Received: 28 Jun 2025
Accepted: 23 Jul 2025
Published online: 10 Oct 2025 *

Title: Mask-embedded transformer for English text recognition and correction

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