LEADER 05214nam 22006014a 450 001 9910145585503321 005 20170810185749.0 010 $a1-281-13472-4 010 $a9786611134723 010 $a0-470-17653-9 010 $a0-470-17652-0 035 $a(CKB)1000000000408894 035 $a(EBL)331380 035 $a(OCoLC)191726245 035 $a(SSID)ssj0000120066 035 $a(PQKBManifestationID)11146437 035 $a(PQKBTitleCode)TC0000120066 035 $a(PQKBWorkID)10081111 035 $a(PQKB)11484239 035 $a(MiAaPQ)EBC331380 035 $a(EXLCZ)991000000000408894 100 $a20070316d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCharacter recognition systems$b[electronic resource] $ea guide for students and practioners /$fMohamed Cheriet ... [et al.] 210 $aHoboken, N.J. $cWiley-Interscience$dc2007 215 $a1 online resource (360 p.) 300 $aDescription based upon print version of record. 311 $a0-471-41570-7 320 $aIncludes bibliographical references and index. 327 $aCHARACTER RECOGNITION SYSTEMS; CONTENTS; Preface; Acknowledgments; List of Figures; List of Tables; 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 Preprocessing; 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 327 $a2.2.3 Local Feature Thresholding-Stroke-Based Model2.2.4 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 Preprocessing; 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; 3 Feature Extraction, Selection, and Creation 327 $a3.1 Feature Extraction3.1.1 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 327 $a4.1 Overview of Classification Methods4.2 Statistical Methods; 4.2.1 Bayes Decision Theory; 4.2.2 Parametric Methods; 4.2.3 Nonparametric Methods; 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 327 $a4.6 Combining Multiple Classifiers4.6.1 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 327 $a5.3.4 Strategies for Large Vocabulary 330 $a""Much of pattern recognition theory and practice, including methods such as Support Vector Machines, has emerged in an attempt to solve the character recognition problem. This book is written by very well-known academics who have worked in the field for many years and have made significant and lasting contributions. The book will no doubt be of value to students and practitioners.""-Sargur N. Srihari, SUNY Distinguished Professor, Department of Computer Science and Engineering, and Director, Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, The Sta 606 $aOptical character recognition devices 608 $aElectronic books. 615 0$aOptical character recognition devices. 676 $a006.4/24 676 $a006.424 701 $aCheriet$b M$g(Mohamed)$0981335 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145585503321 996 $aCharacter recognition systems$92239874 997 $aUNINA