Character Recognition Systems: A Guide for Students and Practitioners

Mohamed Cheriet, Nawwaf Kharma, Cheng-Lin Liu, Ching Suen

Character Recognition Systems: A Guide for Students and Practitioners
  • 出版商: Wiley
  • 出版日期: 2007-10-01
  • 售價: $3,980
  • 貴賓價: 9.5 折 $3,781
  • 語言: 英文
  • 頁數: 360
  • 裝訂: Hardcover
  • ISBN: 0471415707
  • ISBN-13: 9780471415701
  • 相關分類: Text-mining

    • 立即出貨 (庫存=1)

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Description

Character Recognition Systems discusses character recognition, a process by which text-based input patterns produce meaningful output. Through the use of practical examples and theoretical concepts, the authors discuss feature extraction and selection, pattern recognition, as well as relevant issues such as the recognition of bank accounts, signature verification, and postal address recognition.

Table of Contents

Figures.

List of Tables.

Preface.

Acknowledgments.

Acronyms.

1. Introduction: Character Recognition, Evolution and Development.

1.1 Generation and Recognition of Characters.

1.2 History of OCR.

1.3 Development of New Techniques.

1.4 Recent Trends and Movements.

1.5 Organization of the Remaining Chapters.

References.

2. Tools for Image Pre-Processing.

2.1 Generic Form Processing System.

2.2 A Stroke Model for Complex Background Elimination.

2.2.1 Global Gray Level Thresholding.

2.2.2 Local Gray Level Thresholding.

2.2.3 Local Feature Thresholding-Stroke Based Model.

2.2.4 Choosing the Most Efficient Character Extraction Method.

2.2.5 Cleaning up Form Items Using Stroke Based Model.

2.3 A Scale-Space Approach for Visual Data Extraction.

2.3.1 Image Regularization.

2.3.2 Data Extraction.

2.3.3 Concluding Remarks.

2.4 Data Pre-Processing.

2.4.1 Smoothing and Noise Removal.

2.4.2 Skew Detection and Correction.

2.4.3 Slant Correction.

2.4.4 Character Normalization.

2.4.5 Contour Tracing/Analysis.

2.4.6 Thinning.

2.5 Chapter Summary.

References 72.

3. Feature Extraction, Selection and Creation.

3.1 Feature Extraction.

3.1.1 Moments.

3.1.2 Histogram.

3.1.3 Direction Features.

3.1.4 Image Registration.

3.1.5 Hough Transform.

3.1.6 Line-Based Representation.

3.1.7 Fourier Descriptors.

3.1.8 Shape Approximation.

3.1.9 Topological Features.

3.1.10 Linear Transforms.

3.1.11 Kernels.

3.2 Feature Selection for Pattern Classification.

3.2.1 Review of Feature Selection Methods.

3.3 Feature Creation for Pattern Classification.

3.3.1 Categories of Feature Creation.

3.3.2 Review of Feature Creation Methods.

3.3.3 Future Trends.

3.4 Chapter Summary.

References.

4. Pattern Classification Methods.

4.1 Overview of Classification Methods.

4.2 Statistical Methods.

4.2.1 Bayes Decision Theory.

4.2.2 Parametric Methods.

4.2.3 Non-ParametricMethods.

4.3 Artificial Neural Networks.

4.3.1 Single-Layer Neural Network.

4.3.2 Multilayer Perceptron.

4.3.3 Radial Basis Function Network.

4.3.4 Polynomial Network.

4.3.5 Unsupervised Learning.

4.3.6 Learning Vector Quantization.

4.4 Support Vector Machines.

4.4.1 Maximal Margin Classifier.

4.4.2 Soft Margin and Kernels.

4.4.3 Implementation Issues.

4.5 Structural Pattern Recognition.

4.5.1 Attributed String Matching.

4.5.2 Attributed Graph Matching.

4.6 Combining Multiple Classifiers.

4.6.1 Problem Formulation.

4.6.2 Combining Discrete Outputs.

4.6.3 Combining Continuous Outputs.

4.6.4 Dynamic Classifier Selection.

4.6.5 Ensemble Generation.

4.7 A Concrete Example.

4.8 Chapter Summary.

References.

5. Word and String Recognition.

5.1 Introduction.

5.2 Character Segmentation.

5.2.1 Overview of Dissection Techniques.

5.2.2 Segmentation of Handwritten Digits.

5.3 Classification-Based String Recognition.

5.3.1 String Classification Model.

5.3.2 Classifier Design for String Recognition.

5.3.3 Search Strategies.

5.3.4 Strategies for Large Vocabulary.

5.4 HMM-Based Recognition.

5.4.1 Introduction to HMMs.

5.4.2 Theory and Implementation.

5.4.3 Application of HMMs to Text Recognition.

5.4.4 Implementation Issues.

5.4.5 Techniques for Improving HMMs’ Performance.

5.4.6 Summary to HMM-Based Recognition.

5.5 Holistic Methods For Handwritten Word Recognition.

5.5.1 Introduction to Holistic Methods.

5.5.2 Overview of Holistic Methods.

5.5.3 Summary to Holistic Methods.

5.6 Chapter Summary.

References.

6. Case Studies.

6.1 Automatically Generating Pattern Recognizers with Evolutionary Computation.

6.1.1 Motivation.

6.1.2 Introduction.

6.1.3 Hunters and Prey.

6.1.4 Genetic Algorithm.

6.1.5 Experiments.

6.1.6 Analysis.

6.1.7 Future Directions.

6.2 Offline Handwritten Chinese Character Recognition.

6.2.1 Related Works.

6.2.2 System Overview.

6.2.3 Character Normalization.

6.2.4 Direction Feature Extraction.

6.2.5 Classification Methods.

6.2.6 Experiments.

6.2.7 Concluding Remarks.

6.3 Segmentation and Recognition of Handwritten Dates on Canadian Bank Cheques.

6.3.1 Introduction.

6.3.2 System Architecture.

6.3.3 Date Image Segmentation.

6.3.4 Date Image Recognition.

6.3.5 Experimental Results.

6.3.6 Concluding Remarks.

References.

商品描述(中文翻譯)

《字符識別系統》討論了字符識別，一個將基於文本的輸入模式轉換為有意義輸出的過程。通過實際例子和理論概念，作者討論了特徵提取和選擇、模式識別，以及相關問題，如銀行帳戶識別、簽名驗證和郵政地址識別。

目錄：
圖表
表格清單
前言
致謝
縮寫
第1章：介紹：字符識別、演進和發展
1.1 字符的生成和識別
1.2 光學字符識別的歷史
1.3 新技術的發展
1.4 最新趨勢和動向
1.5 其他章節的組織
參考文獻

第2章：圖像預處理工具
2.1 通用表單處理系統
2.2 複雜背景消除的筆劃模型
2.2.1 全局灰度閾值
2.2.2 局部灰度閾值
2.2.3 基於筆劃模型的局部特徵閾值
2.2.4 選擇最有效的字符提取方法
2.2.5 使用筆劃模型清理表單項目
2.3 用於視覺數據提取的尺度空間方法
2.3.1 圖像正則化
2.3.2 數據提取
2.3.3 結論
2.4 數據預處理
2.4.1 平滑和去噪
2.4.2 傾斜檢測和校正
2.4.3 斜率校正
2.4.4 字符歸一化
2.4.5 輪廓追蹤/分析
2.4.6 細化
2.5 章節總結
參考文獻

第3章：特徵提取、選擇和創建
3.1 特徵提取
3.1.1 矩
3.1.2 直方圖
3.1.3 方向特徵
3.1.4 圖像配準
3.1.5 哈夫變換
3.1.6 基於線的表示
3.1.7 傅立葉描述子
3.1.8 形狀逼近
3.1.9 拓撲特徵
3.1.10 線性變換
3.1.11 核函數
3.2 模式分類的特徵選擇
3.2.1 特徵選擇方法綜述
3.3 模式分類的特徵創建
3.3.1 特徵創建的類別
3.3.2 特徵創建方法綜述
3.3.3 未來趨勢
3.4 章節總結
參考文獻

第4章：模式分類方法
4.1 分類方法概述
4.2 統計方法
4.2.1 貝葉斯決策理論
4.2.2 參數方法
4.2.3 非參數方法
4.3 人工神經網絡
4.3.1 單層神經網絡
4.3.2 多層感知器
4.3.3 徑向基函數網絡
4.3.4 多項式網絡
4.3.5 非監督學習
4.3.6 學習向量量化
4.4 支持向量機
4.4.1 最大間隔分類

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