On-line recognition of English and numerical characters.

by Cheung Wai-Hung Wellis.Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves 52-54).ACKNOWLEDGEMENTSABSTRACTChapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- CLASSIFICATION OF CHARACTER RECOGNITION --- p.1Chapter 1.2 --- HISTORICAL DEVELOPMENT --- p.3Chapter 1.3 --- RECOGNITION METHODOLOGY --- p.4Chapter 2 --- ORGANIZATION OF THIS REPORT --- p.7Chapter 3 --- DATA SAMPLING --- p.8Chapter 3.1 --- GENERAL CONSIDERATION --- p.8Chapter 3.2 --- IMPLEMENTATION --- p.9Chapter 4 --- PREPROCESSING --- p.10Chapter 4.1 --- GENERAL CONSIDERATION --- p.10Chapter 4.2 --- IMPLEMENTATION --- p.12Chapter 4.2.1 --- Stroke connection --- p.12Chapter 4.2.2 --- Rotation --- p.12Chapter 4.2.3 --- Scaling --- p.14Chapter 4.2.4 --- De-skewing --- p.15Chapter 5 --- STROKE SEGMENTATION --- p.17Chapter 5.1 --- CONSIDERATION --- p.17Chapter 5.2 --- IMPLEMENTATION --- p.20Chapter 6 --- LEARNING --- p.26Chapter 7 --- PROTOTYPE MANAGEMENT --- p.27Chapter 8 --- RECOGNITION --- p.29Chapter 8.1 --- CONSIDERATION --- p.29Chapter 8.1.1 --- Delayed Stroke Tagging --- p.29Chapter 8.1.2 --- Bi-gram --- p.29Chapter 8.1.3 --- Character Scoring --- p.30Chapter 8.1.4 --- Ligature Handling --- p.32Chapter 8.1.5 --- Word Scoring --- p.32Chapter 8.2 --- IMPLEMENTATION --- p.33Chapter 8.2.1 --- Simple Matching --- p.33Chapter 8.2.2 --- Best First Search Matching --- p.33Chapter 8.2.3 --- Multiple Track Method --- p.35Chapter 8.3 --- SYSTEM PERFORMANCE TUNING --- p.37Chapter 9 --- POST-PROCESSING --- p.38Chapter 9.1 --- PROBABILITY MODEL --- p.38Chapter 9.2 --- WORD DICTIONARY APPROACH --- p.39Chapter 10 --- SYSTEM IMPLEMENTATION AND PERFORMANCE --- p.41Chapter 11 --- DISCUSSION --- p.43Chapter 12 --- EPILOG --- p.47Chapter APPENDIX I - --- PROBLEMS ENCOUNTERED AND SUGGESTED ENHANCEMENTS ON THE SYSTEM --- p.48Chapter APPENDIX II - --- GLOSSARIES --- p.51REFERENCES --- p.5

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Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters