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. A fully connected neural network is then used to evaluate our approach on 16 datasets encompassing 132 unique handshape classes from various sign languages. The results demonstrate high accuracy and strong generalization across datasets, indicating that SignWriting can serve as a structured feature space for multilingual handshape recognition. Our findings underscore the potential of SignWriting to enhance cross-linguistic gesture recognition, contributing to more inclusive and accessible technologies. The source code is publicly available at: https://anonymous.4open.science/r/signwriting-recognition-05C7.