There　are　many　problems　in　American　license　plates,　such　as　varied　background　patterns　and　complex　text　information.　Traditional　license　plate　recognition　methods　are　difficult　to　meet　the　demand　for　different　types　of　license　plate　recognition　problems.　Through　the　research　of　the　connectionist　text　proposal　network　(CTPN)　and　convolutional　recurrent　neural　network　(CRNN),　an　American　license　plate　detection　and　recognition　method　was　proposed　in　this　study.　The　SE-MobileNetV2　fast　feature　extraction　model　was　proposed　to　greatly　improve　the　speed　of　feature　extraction.　Aiming　at　the　situation　that　many　text　boxes　were　produced　in　American　license　plate　detection,　and　that　some　license　plate　numbers　were　not　in　the　same　text　box　because　of　the　pattern　partition,　an　anchor　box　method　for　screening　license　plate　numbers　was　designed,　which　used　Adam　optimization　algorithm　to　train　CRNN.　We　built　an　American　license　plate　data　set　and　carried　out　verification　experiments.　The　recognition　rates　of　state　name,　license　plate　number　and　common　recognition　rate　reached　92%,　84%　and　82%　respectively.　Moreover,　the　network　model　proposed　in　this　study　was　less　than　60　MB　in　size,　having　strong　real-time　performance.　©　2023　Editorial　Board　of　Journal　of　Harbin　Engineering.　All　rights　reserved.