Class-based n-gram models of natural language
Computational Linguistics
Statistical methods for speech recognition
Statistical methods for speech recognition
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Integration of an On-Line Handwriting Recognition System in a Smart Phone Device
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Sentence Recognition through Hybrid Neuro-Markovian Modeling
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Lexical Post-Processing Optimization for Handwritten Word Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
N-Gram and N-Class Models for On line Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
N-Gram Language Models for Offline Handwritten Text Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Morpho-syntactic post-processing of N-best lists for improved French automatic speech recognition
Computer Speech and Language
Language models for online handwritten Tamil word recognition
Proceeding of the workshop on Document Analysis and Recognition
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This paper investigates the integration of a statistical language model into an on-line recognition system in order to improve word recognition in the context of handwritten sentences. Two kinds of models have been considered: n-gram and n-class models (with a statistical approach to create word classes). All these models are trained over the Susanne corpus and experiments are carried out on sentences from this corpus which were written by several writers. The use of a statistical language model is shown to improve the word recognition rate and the relative impact of the different language models is compared. Furthermore, we illustrate the interest to define an optimal cooperation between the language model and the recognition system to re-enforce the accuracy of the system.