Arbitrary　shape　text　detection　is　a　challenging　task　in　scene　text　recognition.　Driven　by　deep　learning　and　large-scale　data　sets,　the　detection　method　based　on　connected　component　(CC)　has　increasingly　gained　popularity.　However,　there　are　still　problems　of　unclear　separation　of　text　instances　and　incorrect　component　links.　Thus,　in　this　paper,　the　authors　propose　a　novel　component　connection　method,　that　is,　Fragmented　Affinity　Reasoning　Network　of　Text　Instances　(FARNet),　for　arbitrary　shape　text　detection.　The　network　consists　of　a　Weighted　Feature　Fusion　Pyramid　Network　(WFFPN),　Text　Fragments　Subgraph　(TFS),　and　Dense　Graph　Attention　Network　(DGAT),　which　can　be　trained　end-to-end.　The　WFFPN　is　used　to　generate　text　fragments,　TFS　and　DGAT　jointly　construct　an　affinity　reasoning　network.　Since　the　neighbouring　boundaries　between　text　instances　may　blend　them　into　a　single　instance,　the　core　idea　is　to　use　the　WFFPN　to　divide　the　text　instance　into　a　series　of　rectangular　fragments,　the　affinity　reasoning　network　infers　the　affinity　between　fragments　and　then　links　them　to　rebuild　text　instances.　Extensive　experiments　on　seven　challenging　datasets　(ICDAR2015,　MSRA-TD500,　Totaltext,　CTW-1500,　ICDAR　2019MLT,　ICDAR2019　ArT,　and　DAST-1500)　demonstrate　that　the　proposed　text　detector　achieves　state-of-the-art　performance　in　both　on　polygon　datasets　and　quadrilateral　datasets.　The　code　is　available　at　https://github.com/giganticpower/FARNet.　©　2023　The　Authors.　IET　Image　Processing　published　by　John　Wiley　&　Sons　Ltd　on　behalf　of　The　Institution　of　Engineering　and　Technology.