Ensemble CNN with Test Time Augmentation for MNIST Digit Recognition — A Top 6% Kaggle Solution

Accuracy: 0.99628 · Rank: 72 / 1,181 · Kaggle Digit Recognizer Competition Problem Framing MNIST is a solved problem in the academic sense — state-of-the-art models have exceeded human-level performance on it for years. The challenge in a competitive context is not whether a CNN can classify handwritten digits, but how much variance you can squeeze out of an already high-performing system when the ceiling is 1.0, and the marginal gains are measured in the fourth decimal place. At 99.6%+ accuracy, a single misclassified digit per 200 samples is the difference between medal territory and the middle of the leaderboard. The solution presented here addresses this precision problem through two compounding mechanisms: ensemble diversity to reduce variance across model architectures, and Test Time Augmentation (TTA) to reduce variance across the inference distribution. The combination pushed a single-model baseline of 0.995035 to a final leaderboard score of 0.99628. Data Preprocessing Pipelin