Comparative Performance Analysis of Software-Based Restoration Techniques for NAVTEX Message

Maritime transportation requires reliable navigational safety communications to ensure vessel safety and operational efficiency. The Maritime Single Window (MSW) enables vessels to submit all maritime data digitally without human intervention. NAVTEX (Navigational Telex) messages provide navigational warnings, meteorological warnings and forecasts, piracy, and search and rescue information that require integration into automated MSW system. However, NAVTEX transmissions experience message corruption when Forward Error Correction (FEC) mechanisms fail, marking unrecoverable characters with asterisks. Current standards require discarding messages exceeding 4% error rates, resulting in safety information loss. Traditional human interpretation of corrupted messages creates limitations that prevent automated MSW integration. This paper presents the application of Masked Language Modeling (MLM) with Transformer encoders for automated NAVTEX message restoration. Our approach treats asterisk characters as masked tokens, enabling bidirectional context processing to reconstruct corrupted characters. We evaluated MLM against dictionary-matching and n-gram models using 69,658 NAVTEX messages with corruption ranging from 1% to 33%. MLM achieved 85.4% restoration rate versus 44.4–64.0% for statistical methods. MLM maintained residual error rates below the 4% threshold for initial corruption up to 25%, while statistical methods exceeded this limit at 10%. This automated restoration capability supports MSW integration while preserving critical safety information during challenging transmission conditions.