In　the　end-to-end　text　recognition　of　complex　natural　scenes,　because　text　and　background　are　difficult　to　distinguish,　the　location　information　detected　by　text　and　the　semantic　information　recognized　do　not　match,　and　the　correlation　between　detection　and　recognition　cannot　be　effectively　utilized.　In　response　to　this　problem,　this　paper　proposes　a　multi-party　synergetic　information　with　dual-domain　awareness　text　spotting　(MSIDA).　By　enhancing　text　region　features　and　edge　textures,　the　synergies　between　text　detection　and　recognition　features　are　utilized　to　improve　end-to-end　text　recognition　performance.　Firstly,　a　dual-domain　awareness　(DDA)　module　integrating　text　space　and　direction　information　is　designed　to　enhance　the　visual　feature　information　of　text　instances.　Secondly,　a　multi-party　explicit　information　synergy　(MEIS)　is　proposed　to　extract　explicit　information　from　coding　features　and　generate　candidate　text　instances　by　matching　and　allocating　the　position,　classification　and　character　multi-party　information　used　for　detection　and　recognition.　Finally,　cooperative　features　guide　learnable　query　sequences　through　decoders　to　obtain　text　detection　and　recognition　results.　Compared　to　the　latest　decoder　with　explicit　points　solo　(DeepSolo)　method,　on　the　Total-Text,　ICDAR　2015　and　CTW1500　datasets,　the　accuracy　of　MSIDA　improved　respectively　by　0.8%,　0.8%　and　0.4%.　The　code　and　datasets　are　available　at　https://github.com/msida2024/MSIDA.git.　©　2025　Chinese　Institute　of　Electronics.　All　rights　reserved.