On the Dependence of Handwritten Word Recognizers on Lexicons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of the Number of Writing Variants with an HMM based Cursive Word Recognition System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-level background initialization using Hidden Markov Models
IWVS '03 First ACM SIGMM international workshop on Video surveillance
On the structure of hidden Markov models
Pattern Recognition Letters
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Directional features in online handwriting recognition
Pattern Recognition
EURASIP Journal on Applied Signal Processing
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A new distance measure for hidden Markov models
Expert Systems with Applications: An International Journal
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Similarity-based clustering of sequences using hidden Markov models
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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Abstract: We propose a novel similarity measure for Hidden Markov Models (HMMs). This measure calculates the Bayes probability of error for HMM state correspondences and propagates it along the Viterbi path in a similar way to the HMM Viterbi scoring. It can be applied as a tool to interpret misclassifications, as a stop criterion in iterative HMM training or as a distance measure for HMM clustering. The similarity measure is evaluated in the context of online handwriting recognition on lower case character models which have been trained from the UNIPEN database. We compare the similarities with experimental classifications. The results show that similar and misclassified class pairs are highly correlated. The measure is not limited to handwriting recognition, but can be used in other applications that use HMM based methods.