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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善(Chiou-Shann Fuh) | |
| dc.contributor.author | Yi Lu | en |
| dc.contributor.author | 盧毅 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:13:29Z | - |
| dc.date.available | 2015-08-25 | |
| dc.date.copyright | 2015-08-25 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-18 | |
| dc.identifier.citation | [1] T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
[2] E. Borovikov, “A Survey of Modern Optical Character Recognition,” http://arxiv.org/abs/1412.4183, 2015. [3] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, Vol. 1, pp. 886–893, 2005. [4] P. Domingos, “A Unified Bias-Variance Decomposition,” Proceedings of International Conference on Machine Learning, Stanford, CA, pp. 231-238, 2000. [5] L. Eldén, Matrix Methods in Data Mining and Pattern Recognition, Vol. 4. SIAM, Philadelphia, Pennsylvania, 2007. [6] The Euro Information Website, “The Euro Information Website,” http://www.ibiblio.org/theeuro/InformationWebsite.htm?http://www.ibiblio.org/theeuro/bnk.serialnumbers.htm, 2015. [7] S. H. Friedberg, Linear Algebra, 4th Edition, Pearson, London, UK, 2002. [8] J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, Vol. 29, No. 5, pp. 1189-1232, 2001. [9] S. Fortmann-Roe, “Understanding the Bias-Variance Tradeoff,” http://scott.fortmann-roe.com/docs/BiasVariance.html, 2015. [10] T. Hastie et al., The Elements of Statistical Learning, Vol. 2. No. 1. Springer, New York, 2009. [11] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324, 1998. [12] V. D. Maaten, Laurens, and G. Hinton, “Visualizing Data Using T-SNE,” Journal of Machine Learning Research, Vol. 9, pp. 2579-2605, 2008. [13] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002. [14] C. Olah, “Visualizing MNIST: An Exploration of Dimensionality Reduction,” http://colah.github.io/posts/2014-10-Visualizing-MNIST/, 2015. [15] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 8, No. 1, pp. 62-66, 1979. [16] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825-2830, 2011. [17] C. Rossant, “An Illustrated Introduction to the t-SNE Algorithm,” https://beta.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm, 2015. [18] A. Singh, K. Bacchuwar, and A. Bhasin, “A Survey of OCR Applications,” International Journal of Machine Learning and Computing, Vol. 2, No. 3, pp. 314-318, 2012. [19] Wikipedia, “Optical Character Recognition,” http://en.wikipedia.org/wiki/Optical_character_recognition, 2015. [20] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, pp. 1-18, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52388 | - |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:13:29Z (GMT). No. of bitstreams: 1 ntu-104-R02922144-1.pdf: 5784371 bytes, checksum: 29aa3c446ff0fd1ad897b15e2ca6d8e5 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 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 | |
| dc.language.iso | zh-TW | |
| dc.subject | 矩陣分解 | zh_TW |
| dc.subject | 數鈔機 | zh_TW |
| dc.subject | 稀疏表示 | zh_TW |
| dc.subject | 紙鈔序號辨識 | zh_TW |
| dc.subject | 數據可視化 | zh_TW |
| dc.subject | 梯度提升決策樹 | zh_TW |
| dc.subject | 光學字元識別 | zh_TW |
| dc.subject | Data Visualization | en |
| dc.subject | Currency Serial Number Recognition | en |
| dc.subject | Banknote Counting Machine | en |
| dc.subject | Matrix Factorization | en |
| dc.subject | Gradient Boosting Decision Tree | en |
| dc.subject | Sparse Representation | en |
| dc.subject | Optical Character Recognition | en |
| dc.title | 紙鈔序號辨識 | zh_TW |
| dc.title | Currency Serial Number Recognition | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李昌鴻(Chang-Hong Li),鄭文欽(Wen-Chin Cheng) | |
| dc.subject.keyword | 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, | zh_TW |
| dc.subject.keyword | Optical Character Recognition,Currency Serial Number Recognition,Banknote Counting Machine,Matrix Factorization,Gradient Boosting Decision Tree,Sparse Representation,Data Visualization, | en |
| dc.relation.page | 53 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-08-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-104-1.pdf 未授權公開取用 | 5.65 MB | Adobe PDF |
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