Handwritten　character　recognition　has　been　acknowledged　and　achieved　more　prominent　attention　in　pattern　recognition　research　community　due　to　enormous　applications　&　vagueness　in　application　methods,　while　cloud　computing　delivers　appropriate,　on-demand　access　of　network　to　a　joint　tarn　of　configurable　computing　resource　&　digital　devices.　Principally　two　steps,　feature　extraction　&　character　recognition,　are　required　for　Handwritten　Digit　Recognition　(HDR),　which　are　primarily　based　on　some　classification　algorithms.　Previous　studies　show　the　nonexistence　of　higher　precision　and　truncated　computational　swiftness　for　HDR　procedure.　＂The　projected　research　aimed　to　make　the　trail　towards　digitalization　clearer　by　providing　high　accuracy　and　faster　cloud-based　computational　for　handwritten　digits　recognition.　The　current　study　utilized　a　cloud-based　neural　network　(CNN)　as　a　classifier,　suitable　parameters　of　dataset　MNIST　for　testing　and　training　purposes　as　a　framework　called　DL4J　for　cloud-based　handwritten　digit　recognition.　The　said　system　magnificently　managed　to　obtained　precision　up　to　99.41%,　which　is　higher　than　previously　projected　systems.　Additionally,　the　proposed　method　decreases　cost　and　computational　time　significantly　as　using　cloud-based　architecture　for　testing　and　training;　as　a　result,　the　algorithm　becomes　more　efficient.