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Handwritten Digit Recognition Is One Of The Most Widely Studied Problems In The Field Of Computer Vision And Pattern Recognition, With Applications Ranging From Automated Postal Sorting And Bank Check Processing To Digital Form Analysis And Authentication Systems. Traditional Manual Recognition Methods Are Time-consuming, Error-prone, And Unsuitable For Large-scale Applications. With Recent Advancements In Deep Learning, Significant Progress Has Been Made In Achieving High Accuracy For Digit Classification Tasks. This Study Presents A Handwritten Digit Recognition System That Integrates Deep Learning Networks With OpenCV For Efficient Image Preprocessing, Feature Extraction, And Classification. The Proposed System Employs Techniques Such As Noise Reduction, Thresholding, And Contour Detection To Enhance Image Quality Before Feeding It Into A Deep Neural Network For Accurate Classification. The Model Is Trained And Evaluated Using Benchmark Datasets Such As MNIST To Demonstrate Its Effectiveness. Experimental Results Indicate That The Integration Of OpenCV With Deep Learning Significantly Improves Recognition Accuracy And Computational Efficiency, Making The System Robust And Applicable For Real-world Scenarios.