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研究生:謝竣翔
研究生(外文):Chun-Hsiang Hsieh
論文名稱:以筆畫結構點進行紙本手寫英數字之辨識
論文名稱(外文):Off-line Handwritten Aphanumeric Character Recognition Using Structural Points in Strokes
指導教授:陳桂霞陳桂霞引用關係
指導教授(外文):Guey-Shya Chen
學位類別:碩士
校院名稱:臺中師範學院
系所名稱:教育測驗統計研究所
學門:教育學門
學類:教育測驗評量學類
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:56
中文關鍵詞:影像辨識離線手寫英數字筆劃端點個數筆劃跨越數筆劃跨越距離筆劃跨越位置
外文關鍵詞:Image recognitionOff-line handwritten alphanumeric characterBorder- points in strokesCrossing-count in strokesCrossing-distance in strokesCrossing-position in strokes
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進行紙本手寫字元辨識的研究時,辨識率高低通常與特徵擷取好壞有著密切的關聯。因此在篇本論文當中,將研究著重於特徵的擷取與辨識模組的設計上,希望透過簡單的演算法則,不需經由複雜的數學公式或遞回的處理,就能快速將所需的特徵擷取成功,以提供後續的辨識模組所使用。
本篇論文利用結構式特徵的方法,處理紙本手寫阿拉伯數字與大小寫英文字母之辨識。本文設計的影像辨識系統包含了「影像預前處理」、「特徵擷取」、「字元分類」、「字元辨識」四個階段。其中最重要的是藉由四類特徵,分別為「筆劃端點個數」、「筆劃跨越數」、「筆劃跨越距離」、「筆劃跨越位置」的擷取,計算出每個手寫字元的特徵值。利用這些特徵值的組合,可成功的辨認紙本上手寫的英數字。
本篇論文的研究目標,希望能提出一個簡單且辨識率不錯的辨識演算法,以達到離線手寫字元的辨識目的。透過我們的辨識演算法則,我們請二十位測試者書寫62英數字,包含了10個阿拉伯數字、26大寫英文字母、26個小寫英文字母,總共1240個字,其辨識結果正確率為93.38%,若是除去測試者中書寫草寫小寫字母的因素,那整體的辨識率可提升至97.25%。

When we research into the off-line handwritten alphanumeric characters, we find that there is a closely relationship between the recognition rate and feature extractions. Therefore, this study is emphasized by the character feature extraction and the recognition models design. It is quick to implement this recognition models because the four features we extracted do not need to be calculated by the complex mathematic formulas and be operated by the difficult recursive algorithm.
In this paper, we take the structural features skills to apply to distinguish the off-line handwritten alphanumeric characters including the digits, the uppercase letters and the lowercase letters. This recognition process consists of the following stages, data preprocessing, feature extraction, character classification and character recognition. The most importance of all is to utilize four features, the BPFs, CCFs, CDFs and CPFs, in the classification and recognition procedure to match the characters. By the combination of the mentioned features, we can easily recognize the off-line handwritten alphanumeric characters.
The purpose of this study is designed to get reasonable speed and high accuracy to successfully recognize the handwritten alphanumeric characters. Taking this recognition system, we made a simulation on twenty testers, each of them writing 62 characters comprising the digits, the uppercase letters and the lowercase letters. From the results of the simulation, the recognition rate in these 1240 characters is accurately up to 93.38%. However, if we do not consider the script lowercase letters in the test, the recognition rate will rise to 97.25%.

中文摘要 i
ABSTRACT ii
ACKNOWLEDGMENTS iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1. INTRODUCTION 1
1.1 Motivation 1
1.2 Survey of Related Research 2
1.3 Problem Definition 3
1.4 System Description and Assumptions 3
1.5 Thesis Organization 5
CHAPTER 2. BACKGROUND KNOWLEDGE 7
2.1 History 7
2.2 Regions of Character Recognition 8
2.3 Feature extraction in handwritten Recognition 9
CHAPTER 3. DATA PREPROCESSING 13
3.1 Image Transformation 13
3.2 Character Processing 16
3.3 Character Thinning 19
CHAPTER 4. FEARTUE EXTRACTION 22
4.1 Extraction of Border-points Features 23
4.2 Extraction of Crossing-count Features 24
4.3 Extraction of Crossing-distance Features 26
4.4 Extraction of Crossing-position Features 28
4.5 Character Classification 30
CHAPTER 5. CHARACTER RECOGNITION 32
5.1 Recognition through the BPFs 33
5.2 Recognition through the CCFs 36
5.3 Recognition through the CDFs 37
5.4 Recognition through the CPFs 38
5.5 Ambiguous Cases 39
5.6 Process of the Character Recognition 42
CHAPTER 6. SIMULATION RESULTS 46
6.1 Simulation Results 46
6.2 Simulation Analyses 46
6.3 Summary 48
CHAPTER 7. CONCLUSIONS AND FUTURE WORK 50
7.1 Conclusions 50
7.2 Future works 51
APPENDIX 52
REFERENCES 54

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