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研究生:盧毅
研究生(外文):Yi Lu
論文名稱:紙鈔序號辨識
論文名稱(外文):Currency Serial Number Recognition
指導教授:傅楸善傅楸善引用關係
指導教授(外文):Chiou-Shann Fuh
口試委員:李昌鴻鄭文欽
口試委員(外文):Chang-Hong LiWen-Chin Cheng
口試日期:2015-06-01
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:53
中文關鍵詞:光學字元識別紙鈔序號辨識數鈔機矩陣分解梯度提升決策樹稀疏表示數據可視化
外文關鍵詞:Optical Character RecognitionCurrency Serial Number RecognitionBanknote Counting MachineMatrix FactorizationGradient Boosting Decision TreeSparse RepresentationData Visualization
相關次數:
  • 被引用被引用:1
  • 點閱點閱:158
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文研究了一個應用導向的光學字元辨識問題:紙鈔序號辨識。一個基於統計特徵和線性分類器的方案被提出用於辨識紙鈔上的序號。在ARM9在300MHz低計算能力的數鈔機環境下,此系統可以對序號取得99.5%的準備率同時能達到每分鐘800張的處理速度。同時本文還採用三個高級的機器學習演算法:稀疏模型(Sparse Representation),矩陣分解模型(Matrix Factorization),梯度提升決策樹(Gradient Boosting Decision Tree)來對紙鈔序號的辨識進行研究,三個模型都能取得非常高的準確率並且具有各自的優點。在紙鈔序號辨識上的良好性能表明這三個模型對其他類型的光學字元識別也具有很大的潛力。最後論文對紙鈔字元圖像進行了二維可視化。其顯示此類數據具有嵌入在高維空間的低維隱含結構。這個結果也驗證了稀疏模型與矩陣分解模型所得到的相似結果。

We propose an application-oriented Optical Character Recognition (OCR) method for Currency Serial Number Recognition (CSNR) in this thesis. The corresponding solution based on statistical feature and linear classifier was proposed for this problem. Our proposed system could achieve the accuracy of 99.5% per bill and the speed of 800 bills per minute in the banknote counting machine with low computational power of ARM9 at 300MHz. We also apply three advanced machine learning methods including Sparse Representation (SR), Matrix Factorization (MF), Gradient Boosting Decision Tree (GBDT) for this specific OCR problem. The high recognition capacities of these methods for OCR problem are confirmed. The experiment results in CSNR have shown these methods promising candidates for more general OCR problem. The visualization of currency serial number data revealed the implicit low-dimensional structure of data that is also observed by the analytical results of MF and SR methods.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
Chapter 1 Introduction 1
1.1 The Overview of OCR 1
1.2 The Motivation of CSNR 2
1.3 The Organization Outlines 4
Chapter 2 Data Description 6
Chapter 3 Our CSNR System 10
3.1 The System Architecture 10
3.2 Character Cropping 11
3.3 Character Recognition 13
3.3.1 Feature Extraction 13
3.3.2 Classification 14
3.4 Domain Knowledge Aid 15
Chapter 4 Sparse Representation 17
4.1 Method 17
4.2 Parameter Analysis 19
Chapter 5 Matrix Factorization 21
5.1 Method 21
5.2 Parameter Analysis 23
5.3 Insights: SVD-Based Classifier vs. SR-Based Classifier 29
Chapter 6 Gradient Boosting Machine 31
6.1 Method 31
6.2 Parameter Analysis 32
Chapter 7 Data Visualization 37
7.1 Thoughts on Data Visualization 37
7.2 Visualization of CSNR Data by t-SNE Algorithm 38
Chapter 8 Experimental Results 44
8.1 Experimental Dataset 44
8.2 Experimental Setting 45
8.2.1 Parameters for Features 45
8.2.2 Parameters for Classifiers 45
8.2.3 Environment of embedded system 46
8.3 Accuracy Results 47
Chapter 9 Conclusion and Future Work 49
REFERENCE 51

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