# 臺灣博碩士論文加值系統

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 在某些應用領域中, 例如名片辨識, 我們必須在沒有整篇文件資訊的情況下, 辨識一行文字. 這篇論文中, 我們提供了一種方法, 針對任何一行獨立文字, 做出正確的辨識. 此方法主要包括三部份, 即前處理、文字辨識核心和後處理. 首先將一行單行的二值化影像做水平校正, 控制傾斜角度於 0.3 度以內, 然後 偵測此行文字是否為斜體字. 在抽取所有的連通元件 (connected components) 之後 , 經過適當的合併與去雜訊處理, 根據先前偵測到的傾斜角度做垂直方向的平移, 然後平滑化. 抽取出來的元件, 由一個「雙核心」架構的核心程式辨識, 視其為斜體或正體 而定, 由這兩個核心其中之一做辨識, 並且, 嘗試切割辨識結果較差之元件, 因為 某些元件可能包含不止一個字元, 而是多個字元相連而成. 切割的方法是利用搜尋 樹的 branch-and-bound 先深 (depth-first) 搜尋. 最後, 元件的垂直位置與字元高度可用來檢查辨識結果. 將一些不可能的字元 排除之後, 正確的字元就可以提升到第一名. 此外, 我們提出了一個決定空白字元 的方法. 對於某些大小寫外型相同的字元, 我們也可以由其垂直位置與字元高度來 判斷其為大寫或小寫. 我們從 107 張英文名片上剪取 646 行的單行文字, 作為測試樣本. 水平校正的 正確率為 99.23%; 斜體字判斷的正確率為 100%, 相連文字有 93.18% 被正確地 切割出來. 核心方面, 正體與斜體的正確率分別達到了 99.07% 與 98.53%.
 In this thesis, we design a procedure for recognizing single text lines. In certain applications, single text lines are to be recognized without any whole-document information. This procedure consists of three parts: pre-processing, character recognition kernel, and post-processing. In the first phase, the skewing angle and italicness of the binarized image of a single text line are detected. After all connected components being extracted and proper combination/deletion, the vertical positions of components are shifted. Images are smoothed then. The components are to be recognized and, if necessary, segmented, using a dual-kernel according yto whether it is an italic text line or a roman one. Touching charcters are segmented using branch-and-bound tree traversal. Finally, vertical position information is used to post-process the recognition results. Some impossibilities are rejected and the correct class is eventually promoted to the first candidate. An approach to determining space characters using the profile is introduced. Characters that have the same shape in capital and lower case are justified according to their heights. In our experiments, we tested 646 text lines cut from English business name cards. The accuracy of skewing-angle detection was 99.23%. The accuracy of italicness detection was 100%. 93.18% of touching characters were correctly segmented. The character recognition rates for correctly segmented or un-touched roman and italic characters were 99.07 and 98.53 respectively.
 CHAPTER 1. INTRODUCTION 1.1 Motivation 1.2 Problem Definition 1.3 Survey of Related Research 1.4 System Description and Assumptions 1.5 Thesis Organization CHAPTER 2. RECOGNITION OF TEXT IN A SINGLE LINE 2.1 Introduction 2.2 Skewing Angle Detection 2.3 Detection of Italicness 2.4 Smoothing 2.5 Connected Components Extraction and De-skewing 2.6 Touching Character Segmentation and Post-Processing 2.7 Space Character Determination 2.8 Upper/Lower Case Determination CHAPTER 3. MULTI-FONT CHARACTER RECOGNITION 3.1 Introduction 3.2 Dual-Kernel Architecture 3.3 Training of the Roman Kernel 3.4 Training of the Italic Kernel 3.5 Post Processing CHAPTER 4. EXPERIMENTAL RESULTS AND ANALYSIS 4.1 Introduction 4.2 Test Images 4.3 Accuracy of Skewing-Angle Detection 4.4 Accuracy of Italicness Detection 4.5 Results of Touching Character Segmentation 4.6 Character Recognition Rate 4.7 Accuracy of Space Character Determination CHAPTER 5. CONCLUSIONS AND FUTURE WORK
 [1] Y.Lu, "Machine printed character segmentation - An overview," Pattern Recognition, Vol. 28, No. 1, pp.67-80, 1995.[2] K. Fukunaga, Introduction to Statistical Pattern Recognition, Second Edition, Academic Press, Inc., 1990.[3] R.C.Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, 1993.[4] C. H. Tung, A Study of Hand-written Chinese Text Recognition, Ph.D. Dissertation, Institute of Computer Science and Information Engineering, National Chiao Tung University, Taiwan, R.O.C., 1994.[5] Y.Lu, "Machine printed character segmentation - An overview," Pattern Recognition, Vol. 28, No. 1, pp.67-80, 1995.[6] Y. H. Chiou, Recognition of Chinese Business Cards, Master Thesis, Institute of Computer Science and Information Engineering, National Chiao Tung University, Taiwan, R.O.C., 1996.[7] Ch. H. Wu, Chinese Hand-written Characters Segmentation in Form Document, Master Thesis, Institute of Computer Science and Information Engineering, National Chiao Tung University, Taiwan, R.O.C., 1997.[8] S. H. Lee, Design of a Business Card Understanding System, Master Thesis, Institute of Computer Science and Information Engineering, National Chiao Tung University, Taiwan, R.O.C., 1998.[9] A. Zramdini and R. Ingold, "Optical Font Recognition Using Typographical Features," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, Auguest 1998.[10]S. Tsujimoto and H. Asada, "Major Components of a Complete Text Reading System," Proc. IEEE, Vol. 80, No. 7, July 1992, pp.1133-1149.[11]R. G. Casey and G. Nagy, "Recursive Segmentation and Classification of Composite Character Patterns," Proc. 6th Int. Conf. Pattern Recognition (Munich, Germany), 1982, pp.1023-1026.[12]W. Niblack, An Introduction to Digial Image Processing, pp.115-116, Prentice Hall, 1986.
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 1 應用共變異矩陣法於IC雷射打印瑕疵檢測之研究 2 中文文件影像分析系統之設計與實現 3 文件影像中之數學方程式擷取 4 表格公文處理系統之設計 5 校園公文處理系統的設計 6 彩色文件影像分割

 1 [40] 李雅明, 吳世全, 陳宏名, " 鐵電記憶元件", 電子月刊, vol.14, No. 9, p. 68, 1996.

 1 已知表格中與線重疊之中文字辨識 2 以人臉視覺資訊作為虛擬實境畫面瀏覽之控制 3 一個使用物件導向技術的視覺化通信協定發展環境 4 用多實例演算法做影像分類與檢索 5 一個支援Web資料庫軟體維護之合作式環境 6 一個有效的於分散式視訊伺服器環境下支援容錯處理之視訊資料擺放方法 7 Web上軟體元件再利用工具之製作 8 通道調適在電話語音辨識上的研究 9 一個有效的分散式視訊伺服器系統之工作排程方法 10 行動式網路下網路層與資料連結層的整合 11 具錯誤更正能力之多媒體傳輸COM原件 12 未知型電腦病毒偵測器之設計與實作 13 在中文OCR系統中偵測並且調整斜體文字 14 連續語音辨認的速度改進研究 15 APER環境程序程式的樹狀表示法

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