Bayesian Network Modeling of Hangul Characters for On-line Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Handwritten Hangul Character Recognition with Hierarchical Stochastic Character Representation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Utilization of Hierarchical, Stochastic Relationship Modeling for Hangul Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling sequences using pairwise relational features
Pattern Recognition
Document processing with Bayesian network and agent-based programming
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
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Abstract: It is important to model strokes and their relationships for on-line handwriting recognition, because they reflect character structures. We propose to model them explicitly and statistically with Bayesian networks. A character is modeled with stroke models and their relationships. Strokes, parts of handwriting traces that are approximately linear, are modeled with a set of point models and their relationships. Points are modeled with conditional probability tables and distributions for pen status and X, Y positions in the 2-D space, given the information of related points. A Bayesian network is adopted to represent a character model, whose nodes correspond to point models and whose arcs their dependencies. The proposed system was tested on the recognition of on-line handwriting digits. It showed higher recognition rates than the HMM based recognizer with chaincode features and was comparable to other published systems.