使用二階段度量學習的中文離線簽名辨識;Using two-stage metric learning for Chinese offline signature recognition

NCUIR > College of Electrical Engineering & Computer Science > Executive Master of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/93007

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93007

Title:	使用二階段度量學習的中文離線簽名辨識;Using two-stage metric learning for Chinese offline signature recognition
Authors:	黃重霖;Huang, Chong-Lin
Contributors:	資訊工程學系在職專班
Keywords:	離線簽名識別;深度學習;二階段度量學習;三元組損失函數;偽造簽名;Offline signature recognition;deep learning;two-stage metric learning;triple loss function;forged signature
Date:	2023-06-27
Issue Date:	2024-09-19 16:38:17 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究旨在解決人工識別簽名所需的龐大人力與時間成本，以及如何準確、快速檢測偽造簽名的問題。我們提出了一個能夠在辨識中文離線簽名方面表現良好的系統，該系統通過對簽名資料集進行資料前處理，結合現有的深度學習技術，通過二階段度量學習和三元組損失函數對資料集進行模型訓練，最終，我們利用該模型預測輸入簽名影像的相似度距離。在實際應用的簽名例子中，我們實現了平均偵測仿冒準確率為 82%的結果。此一成果可以應用於大型考試的自動化簽名辨識。 ;This study aims to address the significant manpower and time costs required for manual identification of signatures, as well as the issue of accurately and quickly detecting forged signatures. We propose a system that performs well in the recognition of Chinese offline signatures. The system preprocesses the signature dataset, combines existing deep learning techniques, trains the dataset using two-stage metric learning and the triplet loss function, and ultimately uses the model to predict the similarity distance of input signature images. In practical signature examples, we achieved an average detection accuracy of 82% for detecting counterfeits. This achievement can be applied to automated signature recognition in large-scale exams.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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