Character　recognition　from　handwritten　images　has　received　greater　attention　in　research　community　of　pattern　recognition　due　to　vast　applications　and　ambiguity　in　learning　methods.　Primarily,　two　steps　including　character　recognition　and　feature　extraction　are　required　based　on　some　classification　algorithm　for　handwritten　digit　recognition.　Former　schemes　exhibit　lack　of　high　accuracy　and　low　computational　speed　for　handwritten　digit　recognition　process.　The　aim　of　the　proposed　endeavor　was　to　make　the　path　toward　digitalization　clearer　by　providing　high　accuracy　and　faster　computational　for　recognizing　the　handwritten　digits.　The　present　research　employed　convolutional　neural　network　as　classifier,　MNIST　as　dataset　with　suitable　parameters　for　training　and　testing　and　DL4J　framework　for　hand　written　digit　recognition.　The　aforementioned　system　successfully　imparts　accuracy　up　to　99.21%　which　is　higher　than　formerly　proposed　schemes.　In　addition,　the　proposed　system　reduces　computational　time　significantly　for　training　and　testing　due　to　which　algorithm　becomes　efficient.