LeRec: a NN/HMM hybrid for on-line handwriting recognition
Neural Computation
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Off-line handwritten word recognition using a mixed HMM-MRF approach
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Cursive script recognition applied to the processing of bank cheques
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Recognition of handwritten words using stochastic models
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Neural Computation
Interactive paper for radiology findings
Proceedings of the 16th international conference on Intelligent user interfaces
Digital pen in mammography patient forms
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
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We present a Neural Network - Hidden Markov Model Hybrid for the recognition of cursive words which are represented as left-right sequences of graphemes. The proposed approach models words with ergodic HMMs and is designed for small vocabularies. A single neural network provides grapheme observation probabilities for all HMMs in order to compute the most likely word model. During the iterative EM like training of the hybrid, the HMMs provide the targets for the discriminant training of the neural network. An extension of the approach to letter models which can be concatenated in order to form word models and which allow for large vocabularies is also briefly discussed. We report results obtained on a large data base of words from French cheques, showing recognition rates close to 93% for the 30 word vocabulary relevant for French legal amounts.