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Deep learning for printed document source identification
Min-Jen Tsai
*
, Yu Han Tao, Imam Yuadi
*
此作品的通信作者
資訊管理研究所
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引文 斯高帕斯(Scopus)
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Keyphrases
Deep Learning
100%
Printed Documents
100%
Source Identification
100%
Printer
66%
Deep Learning System
66%
Rapid Development
33%
Information Technology
33%
Forensic System
33%
Feature Extraction
33%
Statistical Methods
33%
Feature Selection
33%
Human Interaction
33%
Support Vector Machine
33%
Data Preprocessing
33%
Digital Content
33%
Feature-based
33%
Classification Problem
33%
Convolutional Neural Network
33%
Internet Use
33%
Testing Tools
33%
Forged Document
33%
Best Interests
33%
Image Classification
33%
Extraction Features
33%
Internet Information
33%
Complex Image
33%
Machining Technology
33%
Safety Testing
33%
Shallow Machine Learning
33%
Criminal
33%
Digital Formats
33%
Copyright Infringement
33%
Scanned Documents
33%
Counterfeit Currency
33%
Statistical Support
33%
Document Feature
33%
Computer Science
Source Document
100%
Deep Learning Method
100%
Learning System
75%
Support Vector Machine
50%
Printer
50%
Feature Extraction
50%
Rapid Development
25%
Convolutional Neural Network
25%
Human Interaction
25%
Data Preprocessing
25%
Digital Content
25%
Classification Problem
25%
Feature Selection
25%
document image
25%
Image Classification
25%
Copyright Infringement
25%
Digital Format
25%
Machine Learning
25%
Information Technology
25%
Statistics
25%
Engineering
Deep Learning Method
100%
Learning System
75%
Feature Extraction
50%
Information Technology
25%
Data Preprocessing
25%
Classification Problem
25%
Image Classification
25%
Safety Testing
25%
Complex Image
25%
Digital Format
25%
Convolutional Neural Network
25%
Support Vector Machine
25%