Confusion-Adaptive Margin Learning for Handwritten Letter Recognition: A Controlled Study on EMNIST Letters
Handwritten character recognition systems can achieve high accuracy while retaining concentrated errors among visually similar classes. Motivated by residual letter confusions in earlier geometry-based recognition work, we investigate whether these errors can be addressed through the training objective. We introduce Co...