IEEE Transactions on Pattern Analysis and Machine Intelligence
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Off-Line Cursive Handwriting Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Model for Segmentation Based Word Recognition with Lexicon
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Building Skeletal Graphs for Structural Feature Extraction on Handwriting Images
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
IEEE Transactions on Image Processing
A Human Interactive Proof Algorithm Using Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Synthetic handwritten CAPTCHAs
Pattern Recognition
Leveraging cognitive factors in securing WWW with CAPTCHA
WebApps'10 Proceedings of the 2010 USENIX conference on Web application development
Visual CAPTCHA with handwritten image analysis
HIP'05 Proceedings of the Second international conference on Human Interactive Proofs
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This paper introduces a new stochastic framework of modeling sequences of features that are combinations of discrete symbols and continuous attributes. Unlike traditional hidden Markov models, the new model emits observations on transitions instead of states. In this framework, a feature is first labeled with a symbol and then a set of feature-dependent continuous attributes is associated to give more details of the feature. This two-level hierarchy is modeled by symbol observation probabilities which are discrete and attribute observation probabilities which are continuous. The model is rigorously defined and the algorithms for its training and decoding are presented. This framework has been applied to off-line handwritten word recognition using high-level structural features and proves its effectiveness in experiments.