Precision and clarity are essential qualities of written texts; however, Persian script, rooted in Arabic script, presents unique challenges that can compromise readability and correctness. In particular, the use of space and half-space—specifically the Zero Width Non-Joiner (ZWNJ)—is essential for proper character separation in Persian typography. This research introduces four models for correcting spacing and ZWNJ errors at the character level, thereby improving both readability and textual accuracy. By fine-tuning BERT-based transformer models on Bijankhan and Peykare corpora—comprising over 12.7 million preprocessed and annotated words—and formulating the task as sequence labeling, the best model achieves a macro-average F1-score of 97.26%. An interactive corrector that incorporates user input further improves performance to a macro-average F1-score of 98.38%. These results demonstrate the effectiveness of advanced language models in enhancing Persian text quality and highlight their applicability to real-world natural language processing tasks.