Handwriting analysis method using fine-tuning

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K18017
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 90020:Library and information science, humanistic and social informatics-related
• Research Institution: Keio University
• Principal Investigator: NIITSUMA Masahiro 慶應義塾大学, システムデザイン・マネジメント研究科(日吉), 准教授 (50733135)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 筆跡鑑定 / XA / システムズアプローチ / MBSE / システムエンジニアリング / AI / ファインチューニング / 転移学習

Outline of Final Research Achievements

Due to travel restrictions caused by COVID-19 and delays in computer deliveries due to semiconductor shortages, it was not possible to conduct a comprehensive validation of the actual validity of addressing the issue of improving the accuracy of handwriting identification for individuals with limited handwriting samples through pre-training with large-scale image data. Only partial validation was carried out. The results demonstrated the effectiveness of pre-training using out-of-domain similar data with a specific distance function. Additionally, due to changes in research plans caused by the aforementioned limitations, analysis of the explainability of AI using systems engineering and its relationship with human models was conducted. It was suggested that explainability of systems　including AI could be improved by considering human cognitive characteristics, among other factors.

Free Research Field

XAI,筆跡鑑定、システムズアプローチ

Academic Significance and Societal Importance of the Research Achievements

当該結果は、とりわけサンプルデータの少ない状況におけるクラス分類が必要となるような任意の問題に適応可能な知見を含んでるという意味で、広範囲のドメインに有意義である。さらに、説明可能性を高めるためにシステムズアプローチにより人間の認知モデル等を含めてAIを含むシステムを設計するという手法は、説明可能性が重要な任意の問題に対して適応可能であり、今後ますます重要になるという意味で意義があると考える。