Writing, as an omnipresent form of human communication, permeates nearly every aspect of contemporary life. Consequently, inaccuracies or errors in written communication can lead to profound consequences, ranging from financial losses to potentially life-threatening situations. Spelling mistakes, among the most prevalent writing errors, are frequently encountered due to various factors. This research aims to identify and rectify diverse spelling errors in text using neural networks, specifically leveraging the Bidirectional Encoder Representations from Transformers (BERT) masked language model. To achieve this goal, we compiled a comprehensive dataset encompassing both non-real-word and real-word errors after categorizing different types of spelling mistakes. Subsequently, multiple pre-trained BERT models were employed. To ensure optimal performance in correcting misspelling errors, we propose a combined approach utilizing the BERT masked language model and Levenshtein distance. The results from our evaluation data demonstrate that the system presented herein exhibits remarkable capabilities in identifying and rectifying spelling mistakes, often surpassing existing systems tailored for the Persian language.

本研究解决了现代书面交流中普遍存在的拼写错误问题，特别是在波斯语中的应用不足。采用了结合BERT掩蔽语言模型和Levenshtein距离的新方法，显著提高了拼写错误的识别和纠正能力。实验结果表明，该系统在识别和纠正拼写错误方面的表现优于现有的波斯语拼写纠错系统，展现出其潜在的广泛应用价值。

基于BERT和Levenshtein距离的拼写纠错综合方法