Temporal reasoning based on semi-intervals
Artificial Intelligence
Attributed String Matching by Split-and-Merge for On-Line Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Picture Similarity Retrieval Using the 2D Projection Interval Representation
IEEE Transactions on Knowledge and Data Engineering
An on-line Japanese character recognition method using length-based stroke correspondence algorithm
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An Algorithm for On-Line Strokes Verification of Chinese Characters Using Discrete Features
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Reconstructing the correct writing sequence from a set of chinese character strokes
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
A web-based chinese handwriting education system with automatic feedback and analysis
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
International Journal of Distance Education Technologies
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Due to the complex shapes and various writing styles of Chinese characters, it is a challenge to automatically detect the errors in people's handwriting. In this paper, we use attributed relational graph to represent a Chinese character. To model the spatial relationships between the strokes in a Chinese character, a refined interval relationship that considers more granular levels is proposed. A novel interval neighborhood graph is also proposed to compute the distances among the refined interval relationships. Error-tolerant graph matching is used to locate the stroke production errors, sequence error as well as the spatial relationship errors. We also propose a pruning strategy in order to speed up the graph matching. Experiment results show that our proposed method outperforms existing approaches in terms of accuracy as well as its ability to handle more kinds of handwriting errors in less computational time.