Design and implementation of multistage tree classifier for Chinese character recognition.

Yeung Lap Kei.Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves [14-15]).PREFACEABSTRACTCONTENTChapter §1. --- INTRODUCTIONChapter §1.1 --- The Chinese language --- p.1Chapter §1.2 --- Chinese information processing system --- p.2Chapter §1.3 --- Chinese character recognition --- p.4Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6Chapter §1.5 --- Decision TreeChapter §1.5.1 --- Basic Terminology of a decision tree --- p.7Chapter §1.5.2 --- Structure design of a decision tree --- p.10Chapter §1.6 --- Motivation of the project --- p.12Chapter §1.7 --- Objects of the project --- p.14Chapter §1.8 --- Development environment --- p.14Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNING --- p.15Chapter §3. --- APPROACH 2 - SUPERVISED LEARNINGChapter §3.1 --- Idea --- p.17Chapter §3.2 --- The 3 Corner Code --- p.20Chapter §3.3 --- Feature Extraction & Selection --- p.22Chapter §3.4 --- Decision at Each NodeChapter §3.4.1 --- Statistical Linear Discriminant Analysis --- p.22Chapter §3.4.2 --- Optimization of the Number of Misclassification --- p.24Chapter §3.5 --- ImplementationChapter §3.5.1 --- Training Data --- p.36Chapter §3.5.2 --- Clustering with the Use of SAS --- p.38Chapter §3.5.3 --- Building the Decision Trees --- p.42Chapter §3.5.4 --- Description of the Classifier --- p.45Chapter §3.6 --- Experiments and Testing ResultChapter §3.6.1 --- Performance Parameters being Measured --- p.47Chapter §3.6.2 --- Testing by Resubstitution Method --- p.50Chapter §3.6.3 --- Noise Model --- p.52Chapter §4. --- POSSIBLE IMPROVEMENT --- p.55Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIERChapter §5.1 --- Experimental Results --- p.59Chapter §5.2 --- Conclusion --- p.70Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIERChapter §6.1 --- The Optimal Multistage Tree Classifier --- p.72Chapter §6.2 --- Performance Analysis --- p.73Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATIONChapter §7.1 --- Idea --- p.76Chapter §7.2 --- Description of Algorithm --- p.78Chapter §7.3 --- Performance Analysis --- p.81Chapter §8. --- CONCLUSIONChapter §8.1 --- Advantage of the Classifier --- p.84Chapter §8.2 --- Limitation of the Classifier --- p.85Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENTChapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.86Chapter §9.2 --- Improving the Noise Filtering Technique --- p.87Chapter §9.3 --- The Use of 4 Corner Code --- p.88Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.90Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.91Chapter §9.6 --- The Use of Human Intelligence --- p.92APPENDICESChapter A.1 --- K-MEANSChapter A.2 --- Unsupervised Learning ApproachChapter A.3 --- Other Algorithms (Maximum Distance & ISODATA)Chapter A.4 --- Possible ImprovementChapter A.5 --- Theories on Statistical Discriminant AnalysisChapter A.6 --- Passage used in Testing the Performance of the Classifier with Context ConsiderationChapter A.7 --- A Partial List of Semantically Related Chinese CharactersChapter A.8 --- An Example of Misclassification TableChapter A.9 --- "Listing of the Program ""CHDIS.C"""REFERENC