Optical　character　recognition　is　gaining　immense　importance　in　the　domain　of　deep　learning.　With　each　passing　day,　handwritten　digits　(0-9)　data　are　increasing　rapidly,　and　plenty　of　research　has　been　conducted　thus　far.　However,　there　is　still　a　need　to　develop　a　robust　model　that　can　fetch　useful　information　and　investigate　self-build　handwritten　digit　data　efficiently　and　effectively.　The　convolutional　neural　network　(CNN)　models　incorporating　a　sigmoid　activation　function　with　a　large　number　of　derivatives　have　low　efficiency　in　terms　of　feature　extraction.　Here,　we　designed　a　novel　CNN　model　integrated　with　the　extreme　learning　machine　(ELM)　algorithm.　In　this　model,　the　sigmoid　activation　function　is　upgraded　as　the　rectified　linear　unit　(ReLU)　activation　function,　and　the　CNN　unit　along　with　the　ReLU　activation　function　are　used　as　a　feature　extractor.　The　ELM　unit　works　as　the　image　classifier,　which　makes　the　perfect　symmetry　for　handwritten　digit　recognition.　A　deeplearning4j　(DL4J)　framework-based　CNN-ELM　model　was　developed　and　trained　using　the　Modified　National　Institute　of　Standards　and　Technology　(MNIST)　database.　Validation　of　the　model　was　performed　through　self-build　handwritten　digits　and　USPS　test　datasets.　Furthermore,　we　observed　the　variation　of　accuracies　by　adding　various　hidden　layers　in　the　architecture.　Results　reveal　that　the　CNN-ELM-DL4J　approach　outperforms　the　conventional　CNN　models　in　terms　of　accuracy　and　computational　time.