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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇國棟(Guo-Dung Su) | |
| dc.contributor.author | Hao-Sheng Zhang | en |
| dc.contributor.author | 張浩陞 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:14:11Z | - |
| dc.date.copyright | 2022-09-30 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-22 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84518 | - |
| dc.description.abstract | 近年來，由於光學字元辨識系統的技術逐漸發展成熟，對於人類圖像文字的辨識有更全面的影響。光學字元辨識（OCR）是指對文字資料的圖像檔案進行分析辨識處理，取得文字及版面資訊的過程。在光學中，光學像差是實際成像與理論成像結果的偏差，其中包括球面像差、彗星像差、散光、場曲和畸變。在本篇論文中，我們提出一種光學模擬方式進行光束追蹤，其主要設計方法為利用Zemax光學模擬軟體設計五種光學像差結構，而針對OCR系統內的英文字母進行辨識，在文字圖形結構產生像差影響之後，藉由神經網路的訓練下使用Pytesseract影像辨識程式將圖片文字框字後重新訓練圖片，進而提升文字圖形的辨識率。接著，我們展現所有英文字母辨識率之模擬結果在OCR系統中。利用廣角透鏡的參考設計來驗證OCR系統對於字母辨識的影響。我們也分析與討論藉由改變參數設計對於OCR系統的文字辨識情形。基於神經網路的OCR系統可以明顯降低廣角透鏡設計的複雜性。透鏡的數量從10個減少到6個，而孔徑從0.62毫米增加到0.71毫米。因此從辨識結果中發現，藉由文字框字後而多次訓練下的圖片，能夠提高所有英文字母的辨識準確率，最後我們驗證了所提出的光學模擬方法，也探討基於神經網路中五種光學像差結構對於OCR系統的影響。 | zh_TW |
| dc.description.abstract | In recent years, as the technology of the optical character recognition system has gradually developed and matured, it has had a more comprehensive impact on the recognition of human image characters. Optical Character Recognition (OCR) is the process of analyzing and identifying image files of text data to obtain text and layout information. Optical aberration is the deviation of actual imaging from theoretical imaging results, including spherical aberration, coma, astigmatism, field curvature, and distortion. In this paper, we propose an optical simulation method for ray tracing. The primary design method uses Zemax simulation software to design five optical aberration structures. In recognition, after the structure of text and graphics produces aberration effects, the Pytesseract® image recognition program based on a neural network is used to frame the text and re-train the picture under the neural network training, thereby improving the recognition rate of text and graphics. Next, we show the recognition results of all English letter recognition rates in the OCR system. A reference design of a wide-angle lens is used to verify the impact of the OCR system on letter recognition. We also analyze and discuss the text recognition situation for the OCR system by changing the parameter design. The neural network-based OCR system can significantly reduce the complexity of a wide-angle lens design. The number of lenses can be reduced from ten to six. The aperture can be increased from 0.62 mm to 0.71 mm. Therefore, the recognition results show that the recognition accuracy of all English letters can be enhanced by using the pictures after the text box and repeated training. Finally, we verified the proposed optical simulation method—the influence of optical aberration structure on the OCR system. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:14:11Z (GMT). No. of bitstreams: 1 U0001-2109202221254300.pdf: 2574189 bytes, checksum: 3f38f95708eb0281b9885ffb9e30928f (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES viii LIST OF TABLES xii Chapter 1 Introduction 1 1.1 Optical Character Recognition 1 1.2 Optical Aberration 4 1.3 Neural Network 8 1.4 Thesis Structure Organization 12 Chapter 2 Principle of OCR System 13 2.1 OCR System Overview 13 2.2 Process of OCR System 15 2.2.1 Pre-process 16 2.2.2 Midterm-process 18 2.2.3 Post-process 21 2.3 Application of OCR system 22 Chapter 3 Design and Method in OCR System 26 3.1 Simulation Method and Flow Chart 26 3.2 Simulation Application Tools in Zemax 29 3.3 Training Method after The Text Box 37 Chapter 4 Simulation Results and Discussion 40 4.1 English Letter Recognition Rate in OCR System 40 4.1.1 Letter Recognition Rate under Single Optical Aberration Structure 40 4.1.2 Average Letter Recognition Rate under Combined Optical Aberration Structure 45 4.2 Analysis of the OCR System in a Reference Design 48 4.3 Analysis of the Effect of Changing Parameters on the OCR System 54 4.3.1 Reduce Lens Numbers of the Wide-angle Lens System 54 4.3.2 Increase Aperture of the Wide-angle Lens System 60 4.3.3 Section Summary 64 Chapter 5 Conclusion 65 REFERENCE 67 APPENDIX 71 | |
| dc.language.iso | zh-TW | |
| dc.subject | 光束追蹤 | zh_TW |
| dc.subject | 光學像差 | zh_TW |
| dc.subject | 光學字元辨識 | zh_TW |
| dc.subject | 神經網路 | zh_TW |
| dc.subject | 文字框字 | zh_TW |
| dc.subject | 廣角透鏡 | zh_TW |
| dc.subject | Ray Tracing | en |
| dc.subject | Neural Network | en |
| dc.subject | Optical Character Recognition | en |
| dc.subject | Text Box | en |
| dc.subject | Optical Aberration | en |
| dc.subject | Wide-angle Lens | en |
| dc.title | 基於神經網路中光學像差在OCR系統的影響 | zh_TW |
| dc.title | Influence of Optical Aberrations on Optical Character Recognition System based on Neural Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃定洧(Ding-Wei Huang),于天立(Tian-Li Yu) | |
| dc.subject.keyword | 光學字元辨識,光學像差,光束追蹤,神經網路,文字框字,廣角透鏡, | zh_TW |
| dc.subject.keyword | Optical Character Recognition,Optical Aberration,Ray Tracing,Neural Network,Text Box,Wide-angle Lens, | en |
| dc.relation.page | 75 | |
| dc.identifier.doi | 10.6342/NTU202203769 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-09-23 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 光電工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-09-30 | - |
| 顯示於系所單位： | 光電工程學研究所 | |
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| U0001-2109202221254300.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 2.51 MB | Adobe PDF |
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