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Handshape recognition is a fundamental component of Sign Language Recognition (SLR). However, most existing approaches are language-dependent and require extensive training data, which limits their scalability. To address this limitation, we explore the use of SignWriting as a standardized, language-agnostic representation for handshapes. Our method employs Mediapipe for hand landmark extraction, followed by normalization and data augmentation to enhance robustness.