The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Hidden Markov Models with Multiple Observations-A Combinatorial Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection Algorithm of Connected Segments for On-line Chinese Character Recognition
WAA '01 Proceedings of the Second International Conference on Wavelet Analysis and Its Applications
Model-Based On-Line Handwritten Digit Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
SHARK2: a large vocabulary shorthand writing system for pen-based computers
Proceedings of the 17th annual ACM symposium on User interface software and technology
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Core Points - A Framework For Structural Parameterization
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Using entropy to distinguish shape versus text in hand-drawn diagrams
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Frame deformation energy matching of on-line handwritten characters
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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The automatic recognition of online handwriting is considered from an information theoretic viewpoint. Emphasis is placed on the recognition of unconstrained handwriting, a general combination of cursively written word fragments and discretely written characters. Existing recognition algorithms, such as elastic matching, are severely challenged by the variability inherent in unconstrained handwriting. This motivates the development of a probabilistic framework suitable for the derivation of a fast statistical mixture algorithm. This algorithm exhibits about the same degree of complexity as elastic matching, while being more flexible and potentially more robust. The approach relies on a novel front-end processor that, unlike conventional character or stroke-based processing, articulates around a small elementary unit of handwriting called a frame. The algorithm is based on (1) producing feature vectors representing each frame in one (or several) feature spaces, (2) Gaussian K-means clustering in these spaces, and (3) mixture modeling, taking into account the contributions of all relevant clusters in each space. The approach is illustrated by a simple task involving an 81-character alphabet. Both writer-dependent and writer-independent recognition results are found to be competitive with their elastic matching counterparts.