Attributed String Matching by Split-and-Merge for On-Line Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Vocabulary Recognition of On-Line Handwritten Cursive Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Input for Pen-Based Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line Overlaid-Handwriting Recognition Based on Substroke HMMs
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Improvement on On-Line Japanese Character Recognition System for Visually Disabled Persons
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Architecture of Multiple Algorithm Integration for Real-Time Image Understanding Application
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Hindi handwritten word recognition using HMM and symbol tree
Proceeding of the workshop on Document Analysis and Recognition
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On-line handwriting recognition has continued to persist as a popular research field while pen computing applications are widely used in recent years. This paper proposes a novel hybrid system for one stroke style cursive handwriting character recognition. In the system, user can use fingertip to write various kinds of virtual characters (represented by trajectory of fingertip) such as alpha-numeric characters and Chinese characters through a digital camera based user interface. Without pen-up and pen-down information, the virtual characters are written in one stroke. An on-line and an off-line recognition method for such kind of cursive characters are proposed. A hybrid approach of these two methods is proposed to combine the advantages of both of them. Benefit from the integration, the recognition accuracy was increased from 80.6% (off-line classifier) and 83.4% (on-line classifier) to 90.9% (integrated) for stroke order free one stroke cursive handwriting Chinese characters.