The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algebraic Description of Curve Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Shape Matching with Structural Feature Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural feature extraction using multiple bases
Computer Vision and Image Understanding
Thinning Methodologies for Pattern Recognition
Thinning Methodologies for Pattern Recognition
An Algebraic Approach to Automatic Construction of Structural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of Structural Models Incorporating Discontinuous Transformations
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
An Integration of Online and Pseudo-Online Information for Cursive Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human reading based strategies for off-line Arabic word recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Effective handwriting recognition system using geometrical character analysis algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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On-line recognition algorithms free from writing constraints and high-quality thinning algorithms are important subjects in research on handwriting recognition and are also essential for the integration of off-line and on-line recognition of handwriting. We present an approach to the integration of off-line and on-line recognition of unconstrained handwritten characters by adapting an on-line recognition algorithm to off-line recognition, based on high-quality thinning algorithms. In the experiments, high recognition rate has been attained with a small number of class descriptions (typically one class for one character).