This study examines how much handwritten-character information is retained by a deliberately simple first-order edge representation. Instead of learning spatial filters, each input image is transformed by the fixed Sobel-Feldman operator into signed horizontal and vertical derivative maps, which are independently normalized, flattened, and classified by a multilayer perceptron (MLP). The resulting model therefore separates fixed edge extraction from learned classification and provides a controlled baseline for evaluating the sufficiency of first-order image gradients. In the executed experiments, the Sobel-gradient MLP achieves 98.54 percent test accuracy on MNIST and 92.50 percent on the TensorFlow Datasets (TFDS) EMNIST Letters configuration. Macro F1 scores are 0.9853 and 0.9265, respectively. One-vs-rest ROC analysis further yields micro/macro AUC values of 0.9998/0.9998 on MNIST and 0.9987/0.9982 on EMNIST Letters. Confusion-matrix analysis shows that the remaining errors are concentrated among geometrically similar classes, especially 3/8 and 4/9 for MNIST and I/L and G/Q for EMNIST Letters. These results show that fixed first-order gradients preserve substantial class-discriminative structure, while also revealing the specific ambiguities that remain when recognition is driven by edge geometry alone.

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