ADVANCES IN ORIENTAL DOCUMENT ANALYSIS AND RECOGNITION TECHNIQUES
edited by S-W Lee (Korea University), Y Y Tang (Hong Kong Baptist University) & P S P Wang (Northeastern University)
In recent years, rapid progress has been made in computer processing of oriental languages, and the research developments in this area have resulted in tremendous changes in handwriting processing, printed oriental character recognition, document analysis and recognition, automatic input methodologies for oriental languages, etc. Advances in computer processing of oriental languages can also be seen in multimedia computing and the World Wide Web. Many of the results in those domains are presented in this book.
Contents:
Intriguing Aspects of Oriental Languages (C Y Suen et al.)
The
Generation of Oriental Characters: New Perspectives for Automatic Handwriting Processing (R Plamondon et al.)
A New Synthesizing Method for Handwriting Korean Scripts (D-H Lee & H-G Cho)
Differentiating Between Oriental and European Scripts by Statistical Features (L Lam et al.)
Gray-Scale Nonlinear Shape Normalization Method for Handwritten Oriental Character Recognition (S-Y Kim & S-W Lee)
Distributed Autonomous Agents for Chinese Document Image Segmentation (J Liu & Y Y Tang)
Ink Matching of Cursive Chinese Handwritten Annotations (D P Lopresti et al.)
On-Line Handwritten Chinese Character Recognition Directed by Components with Dynamic Templates (X Xiao & R Dai)
A Reliability Design Methodology for Chinese Character Recognition (Y S Huang et al.)
Typeface Identification for Printed Chinese Characters (Y-H Tseng et al.)
A Self-Organizing Hierarchical Classifier for Multi-Lingual Large-Set Oriental Character Recognition (H-S Park et al.)
Printed Chinese Character Similarity Measurement Using Ring Projection and Distance Transform (P C Yuen et al.)
Segmentation and Recognition of Continuous Handwriting Chinese Text (C Hong et al.)
Network-Based Approach to Korean Handwriting Analysis (B-K Sin & J H Kim)
Comparison of Feature Performance and Its Application to Feature Combination in Off-Line Handwritten Korean Alphabet Recognition (K Seo et al.)
Readership: Graduate students and researchers in computer science.