Characters　recognition　has　gained　significant　attention　in　recent　years　within　the　field　of　artificial　intelligence　and　computer　vision　as　robots　increasingly　engage　in　activities　that　involve　collecting　and　processing　texture　information.　This　paper　presents　a　performance　comparison　among　three　algorithms:　k-nearest　neighbor,　simple　logistic　regression,　and　random　forest,　for　recognizing　the　alphabet　and　numeric　characters.　The　proposed　method　involves　obtaining　random　eigenvalues　through　random　sampling　of　the　training　sets.　Subsequently,　a　decision　tree　is　constructed　based　on　the　obtained　eigenvalues,　and　multiple　decision　trees　are　combined　to　yield　the　final　judgment　result,　thus　mitigating　the　risk　of　overfitting.　The　implementation　of　these　algorithms　is　feasible　using　Python　and　existing　frameworks.　The　experimental　results　demonstrate　that　the　random　forest　algorithm　model　achieved　accurate　recognition　of　simple　alphanumeric　characters,　with　a　detection　accuracy　of　up　to　95.7%　in　a　processing　time　of　64.37 s.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.