A glyph is a specific design for a character in a writing system. Analyzing a glyph’s anatomical features can offer insight into its applications, ancestry, and historical context. However, manually identifying features is a subjective, time-consuming task. In this paper we present the Automated Letter Feature Analyzer (ALFA) system for computationally identifying a glyph’s anatomical features. ALFA uses convolutional neural networks (CNNs) along with other image analysis techniques to evaluate glyphs using both learned patterns and explicit shape metrics. A modular web-based framework was created to efficiently render, capture, and label large glyph image datasets for machine learning tasks. CNNs were trained to detect three anatomical features with overall accuracies between 97% and 99%. The system also achieved an accuracy of 98.45% when counting enclosed spaces and objects in positive space, while glyph weight by quadrant was tuned to concur with visual labeling at 97% accuracy. Results show ALFA is not only useful for collecting glyph images and labeling large image datasets but also can facilitate new research in computational linguistics by offering a way to computationally detect a glyph’s anatomical elements.