At　present,　machine　learning　and　deep　learning　models　are　playing　a　key　role　in　various　domains　of　image　classification　including　handwritten　numeral　images.　Handwritten　digits　recognition　(HDR)　via　machine　learning　has　received　great　attention　of　researchers　due　to　ambiguity　in　learning　methods.　Hitherto,　several　researchers　made　significant　efforts　to　improve　the　recognition　process　by　selecting　appropriate　parameters　and　feature　design.　But　there　is　always　room　for　improvement　in　the　conventional　methods.　Convolutional　neural　network　(CNN)　is　a　deep　neural　network　most　commonly　applied　to　analyze　image　classification,　object　detection,　face　recognition,　etc.　To　execute　the　task　of　HDR,　robust　CNN　architecture　is　used　for　feature　extraction　and　classification.　In　this　paper,　a　Java-based　framework　known　as　Deeplearning4j　(DL4J),　is　used　for　recognition　and　classification　of　the　MNIST　database.　Results　demonstrate　that,　compared　to　existing　techniques,　the　proposed　model　is　superior　in　terms　of　accuracy　for　handwritten　digits　recognition.　©　2020　IEEE.