Abstract
Handwriting verification is a behavioral biometric that matches handwritten characters to determine whether it is written by the same person. Because each person has a different handwriting, it is used by investigative agencies for the purpose of presenting court evidence. However, it cannot be defined as a rule because the standards for visual reading of experts are ambiguous. In other words, different experts can make different decisions for the same pair. Therefore, we propose a handwriting verification method based on artificial intelligence that excludes human subjectivity. For 4 frequently used Korean characters, genuine or imposter pairs of the same character were trained with a Siamese-based ResNet network. The verification accuracy for the trained model was about 80%. Through this experiment, the objectivity of handwriting biometric through deep learning was confirmed, and a basis for comparison with verification performance through human eyes was prepared.
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Acknowledgement
This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIT(NRF-2016M3A9E1915855).
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Jin, K.W., Lee, M.K., Jang, W., Lee, E.C. (2021). Verification of Frequently Used Korean Handwritten Characters Through Artificial Intelligence. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_23
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DOI: https://doi.org/10.1007/978-3-030-68449-5_23
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