Recently,　segmentation-based　approaches　have　been　proposed　to　tackle　arbitrary-shaped　text　detection.　The　trade-off　between　speed　and　accuracy　is　still　a　challenge　that　hinders　its　deployment　in　practical　applications.　Previous　methods　adopt　complex　pipelines　to　improve　accuracy　while　ignoring　inference　speed.　Moreover,　the　performance　of　most　efficient　scene　text　detectors　often　suffers　from　weak　feature　extraction　when　equipping　lightweight　networks.　In　this　paper,　we　propose　a　novel　distillation　method　for　efficient　and　accurate　arbitrary-shaped　text　detection,　termed　kernel-mask　knowledge　distillation.　Our　approach　equips　a　low　computational-cost　visual　transformer　module　(VTM)　and　a　feature　adaptation　layer　to　make　full　use　of　feature　based　and　response-based　knowledge　in　distillation.　More　specifically,　first,　the　text　features　are　obtained　by　aggregating　the　multi-level　information　extracted　in　the　respective　backbones　of　the　teacher　and　student　networks.　Second,　the　text　features　are　respectively　sent　to　the　VTM　to　enhance　the　feature　representation　ability.　Then,　we　distill　the　feature-based　and　response　based　kernel　knowledge　of　the　teacher　network　to　obtain　an　efficient　and　accurate　arbitrary-shaped　text　detection　model.　Extensive　experiments　on　publicly　available　datasets　demonstrate　the　state-of-the-art　performance　of　our　method.　It　is　worth　noting　that　our　method　can　achieve　a　competitive　F-measure　of　86.92%　at　34.5　FPS　on　Total-text.　Code　is　available　at　https://　github.com/giganticpower/KKDnet.