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Fonts are a critical element that determines the perception of any medium. To ensure consistent and culturally appropriate font selection across diverse language groups, a multilingual font matching system is currently in development. This research focuses on leveraging the latest advancements in machine learning and computer vision to deeply understand font characteristics and enhance the accuracy of multilingual font matching. Utilizing the &#x2018;stroke elements&#x2019; of fonts is crucial for this matching, building upon the successful development of a method to calculate similarity between Korean fonts in previous studies. We have applied this approach to the English alphabet, defining distinctive &#x2018;stroke elements&#x2019; and developing a deep learning model for their automatic extraction. Additionally, we evaluate the performance of this stroke element extraction model and discuss strategies to further improve extraction accuracy. This groundwork establishes the basis for multilingual font matching and enables the recommendation of similar fonts using the &#x2019;stroke elements&#x2019; of the English alphabet.

Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet