In　identifying　objects,　people　usually　associate　memory　templates　to　guide　visual　attention　and　determine　the　category　of　an　object.　The　initial　character　images　that　children　learn　are　usually　normal　patterns.　However,　the　variation　in　corresponding　handwritten　patterns　is　quite　large.　To　learn　these　deformed　images　with　large　variance,　current　deep　models　must　involve　millions　of　parameters　for　such　kind　of　classification　tasks　that　seem　much　easier　and　simpler　to　children　who　learn　to　recognize　new　characters　associated　with　their　initially　taught　normal　patterns.　From　the　perspective　of　humans’　perception,　when　people　see　a　new　object,　they　first　think　of　a　template　image　in　their　memory,　which　is　similar　to　the　object.　This　mapping　process　makes　it　easier　for　humans　to　learn　new　objects.　Inspired　by　this　cognitive　association　mechanism,　this　study　developed　a　cognition-inspired　handwritten　character　recognition　model　using　a　proposed　normal　template　mapping　neural　network.　This　model　uses　an　encoder-decoder　architecture　to　build　a　normal　template　mapping　neural　network　that　transforms　handwritten　character　images　of　one　class　to　normalized　characters　similar　to　a　given　printed　template　character　image　representing　that　class.　Then,　a　simple　shallow　classifier　recognizes　these　normalized　images,　which　are　easier　to　classify.　The　experimental　results　show　that　the　proposed　model　completes　handwritten　character　recognition　with　comparable　or　higher　precision　at　a　much　lower　parameter　count　than　current　representative　deep　models.　The　proposed　model　removes　the　individual　styles　of　handwritten　character　images　and　maps　them　to　patterns　similar　to　normal　template　images.　This　greatly　reduces　the　classification　difficulty　and　enables　the　classifier　to　classify　only　known　standard　character　images.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.