Over　the　past　few　decades,　Optical　Character　Recognition　(OCR),　particularly　handwriting　recognition,　has　received　much　attention.　Handwritten　Digits　Recognition　(HDR)　means,　receive　and　comprehend　handwriting　inputs　from　different　sources　for　example　pictures,　touch　screens,　paper　documents,　and　other　devices.　The　field　of　HDR　has　witnessed　rapid　progress　owing　to　the　concurrent　availability　of　cheap　and　well-assembled　computers,　advancements　in　learning　algorithms,　and　availability　of　large　databases.　In　recent　years,　HDR　has　received　much　attention　due　to　ambiguity　in　learning　methods.　The　aim　of　the　current　study　was　to　explore　the　potential　of　Deeplearnig4j　(DL4J)　framework　for　HDR.　DL4J　offers　the　most　appropriate　framework　for　the　identification　of　handwritten　digits.　To　execute　the　task　of　HDR,　Convolutional　Neural　Network　(CNN)　is　implemented.　This　study　measures　the　strength　and　productivity　of　DL4J　for　the　aforementioned　tasks　of　recognition　and　attempts　to　upgrade　the　procedure.　Results　obtained　shows　significant　improvement　in　the　recognition　rates　of　hand-typed　digits.　Though　the　accuracy　and　error　rates　obtained　through　our　proposed　system　(CNN-DL4J)　show　variations,　on　average　the　accuracy　rate　remained　at　97　%.　The　aim　of　the　proposed　endeavor　was　to　make　the　path　towards　digitalization　clearer.　Though　the　purpose　was　only　to　identify　the　digits,　we　can　extend　it　to　deal　with　digits　having　different　sizes,　different　languages　(Urdu,　Arabic,　Persian),　letters,　and　the　task　of　detecting　multidigit　person＇s　handwriting.　Hence,　it　could　reduce　the　typing　need　to　an　extent　that　people　will　be　able　to　convert　their　handwritten　materials　into　digital　form　in　one　click　on　its　picture.　Altogether,　this　investigation　combines　CNN　with　the　DL4J　framework　and　took　MNIST　as　a　standard　dataset　to　accomplish　the　task　of　digit　recognition.　In　addition,　the　test　framework　can　be　assessed　in　the　future　for　the　prospects　of　image　classification　and　such　other　pattern　recognition　tasks.