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研究生:陳嘉亨
研究生(外文):Chia-Heng Chen
論文名稱:利用決策樹方法及直接使用系統字型資料作多種類文字辨識及電子書自動建構
論文名稱(外文):Multi-Class Character Recognition by Decision-Tree Approaches and Direct Use of System Font Data for Automatic Digital Book Construction
指導教授:蔡文祥蔡文祥引用關係
指導教授(外文):Wen-Hsiang Tsai
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學系
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:2002
畢業學年度:90
語文別:英文
論文頁數:108
中文關鍵詞:多種類文字辨識文字型別分類文字辨識系統字型資料決策樹樣板相配電子書
外文關鍵詞:multi-type character recognitioncharacter type classificationoptical character recognitionsystem font datadecision treetemplate matchingdigital book
相關次數:
  • 被引用被引用:2
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  • 下載下載:306
  • 收藏至我的研究室書目清單書目收藏:3
利用影像分析及文字辨識的技巧,我們提出一個可以自動建構電子書的方法。文字辨識的主要工作,是希望能辨識多種類的文字。此方法不需要使用文字影像資料來學習,而是直接使用系統字型來當做參考文字。在我們的方法中,有四個階段:文字型別的分類、文字辨識、書頁版面的分析,以及電子書的建構展示。在文字型別的分類階段,我們處理四種文字型別,第一種型別是標題中的中文字,而其餘三種型別則為文章中的中文字、英數文字和標點符號。我們利用決策樹提出一個對文字型別作分類的方法。在文字辨識的階段中,首先我們提出一個不需學習參考資料而直接使用系統字型資料的方法。接著,針對文章中的中文字,我們提出一個利用決策樹及樣板相配來辨識印刷中文字的方法。而針對標題中的中文字、文章中的英數字和標點符號,我們也提出一個主要是利用樣板相配的辨識方法。在這些方法中,成對的影像組成成分廣泛地被利用來協助文字辨識的工作。在書頁版面的分析階段中,我們利用矩量保持二值化及區塊生長技術,從影像內容中取得所有的連接小塊。針對書頁影像中不同的組成成分,我們使用不同的壓縮技術來壓縮它們,以改善整體的壓縮率。良好的實驗結果,顯示了我們所提出方法的可行性。
Based on image analysis and character recognition techniques, a system for digitizing a printed book automatically into a digital version is proposed. In the major work of character recognition, multi-type characters can be recognized. And no character image data need be used for learning; the system fonts are used as the reference characters directly. There exist four phases in the proposed system processes: character type classification, character recognition, page layout analysis, and digital book construction and display. In the phase of character type classification, four types of characters are dealt with, including Chinese characters in titles, and Chinese, alphanumerical, and punctuation characters in texts. A decision-tree method for classifying these character types is proposed. In the phase of character recognition, a method, which uses directly system font data without reference data learning, is proposed first. For printed Chinese characters in texts, a method to recognize them based on decision trees and template matching is proposed next. And for the other miscellaneous types of characters including Chinese characters in titles, and alphanumerical characters and punctuation characters in texts, a method based mainly on template matching is also proposed to recognize them. In these methods, pairs of image components are used extensively to help the recognition work. In the phase of page layout analysis, all the connected components are segmented out of image contents effectively using moment-preserving thresholding and region-growing techniques. Then, different compression techniques are utilized to reduce the data volumes of different components in the page images to improve the overall compression ratio for the resulting digital book. Good experimental results reveal the feasibility of the proposed methods.
CONTENTS
ABSTRACT (in Chinese) i
ABSTRACT (in English) ii
ACKNOWLEDGEMENTS iii
CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES xi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Survey of Related Studies 2
1.3 Overview of Proposed Approaches 4
1.3.1 Definition of Terminologies 4
1.3.2 Assumptions 5
1.3.3 Brief Description of Proposed System 6
1.3.4 Contributions 9
1.4 Thesis Organization 9
Chapter 2 Classification of Character Types by A Decision-Tree Approach 11
2.1 Introduction 11
2.2 Features for Classification of Character Types 12
2.2.1 Features for Chinese Characters in Titles 12
2.2.2 Features for Chinese Characters in Texts 13
2.2.3 Features for Alphanumerical Characters in Texts 13
2.2.4 Features for Punctuation Characters in Texts 14
2.3 Coarse Classification of Character Types by Horizontal Strips 15
2.4 Detailed Classification of Character Types Using Pairs of Character Components 18
2.4.1 Classification Using Pairs of Type-1x Mixed Character Components 19
2.4.2 Classification Using Pairs of Type-2 Mixed Character Components 22
2.4.3 Classification Using Pairs of Mixed Character Components 25
2.5 Experimental Results 28
Chapter 3 Recognition of Chinese Characters by Decision Trees and Template Matching 33
3.1 Introduction 33
3.2 Character Scaling Before Matching 35
3.3 Features for Chinese Character Recognition 35
3.4 Proposed Approach to Character Recognition by Decision Trees 37
3.4.1 Decision Tree Structure 37
3.4.2 Learning of Cluster Overlapping Ranges 42
3.5 Chinese Character Recognition by Template Matching 43
3.5.1 Match Measure 43
3.5.2 Preprocessing 45
3.5.3 Recognition Process 49
3.6 Experimental Results 51
Chapter 4 Recognition of Miscellaneous Characters by Template Matching 55
4.1 Introduction 55
4.2 Preprocessing 56
4.3 Recognition Process 59
4.4 Experimental Results 60
Chapter 5 Automatic Page Layout Analysis 64
5.1 Introduction 64
5.2 Background Elimination by Moment-Preserving Thresholding 65
5.3 Segmentation of Characters from Image Contents by Region Growing 68
5.4 Construction of Text and Outer Frames 69
5.5 Detection of Repetitive Patterns in Outer Frames 72
5.6 Page Layout Organization 73
5.7 Experimental Results 75
Chapter 6 Data Compression and Automatic Digital Book Construction 82
6.1 Introduction 82
6.2 Data Compression 82
6.2.1 Compression of Repetitive Patterns 83
6.2.2 Compression of Images 83
6.2.3 Coding of Characters 84
6.3 Electronic Book Organization 86
6.4 Display of Book Content for Reading 89
6.5 Experimental Results 89
Chapter 7 Experimental Results and Discussions 92
7.1 Experimental Results 92
7.2 Discussions 93
Chapter 8 Conclusions and Suggestions for Future Works 104
8.1 Conclusions 104
8.2 Suggestions for Future Works 105
References 107
[1] G. Nagy, “Twenty years of document image analysis in PAMI,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 38-62, Jan. 2000.
[2] W. H. Tsai, “Moment-preserving thresholding: a new approach,” Computer Vision, Graphics, and Image Processing, Vol. 29, pp. 377-393, 1985.
[3] Tsai and Chan, “A bottom-up approach to color image document analysis and rearrangement,” Technical Report, Department of Computer and Information Science, National Chiao Tung University, pp. 7-42, Jun. 1999.
[4] Tsai and Hsu, “Automatic electronic book construction from scanned page images by image analysis techniques,” Proceedings of International Computer Symposium, Taipei, Taiwan, Republic of China, pp. D299-311, Dec. 2001.
[5] C. J. Park, J. H. Jeon, T. M. Koo, and H. M. Choi, “An edge-based block segmentation and classification for document analysis with automatic character string extraction,” IEEE on International Conference Systems, Man and Cybernetics, Vol. 1, pp. 707-712, 1996.
[6] S. Mori, C. Y. Suen, and K. Yamamoto, “Historical review of OCR research and development,” Proceedings of the IEEE, Vol. 80, No. 7, pp. 1029-1058, July 1992.
[7] G. Nagy, “Chinese character recognition: a twenty-five-year retrospective,” ICPR 88: 9th Int’l Conf. on Pattern Recognition, Vol. 1, pp. 163-167, 1988.
[8] X. Huang, J. Gu, and Y. Wu, “A constrained approach to multifont Chinese character recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 8, pp. 838-843, Aug. 1993.
[9] A. Zramdini and R. Ingold, “Optical font recognition using typographical features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 877-882, Aug. 1998.
[10] G. K. Wallace, “The JPEG still picture compression standard,” IEEE Transactions on Consumer Electronics, Vol. 38, No. 1, Feb. 1992.
[11] S. H. Chen and W. H. Tsai, “Book content digitization and display for digital library by document image analysis and compression-by-classification techniques,” Proc. of 2000 IPPR Conf. On CVGIP, Taipei, Taiwan, ROC, pp. 23-32, 2000.
[12] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, U.S.A., 1993.
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