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Statistical Language Models for On-line Handwritten Sentence Recognition

Solen Quiniou 1, * Eric Anquetil 1 Sabine Carbonnel 1
* Corresponding author
1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : 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.
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  • HAL Id : hal-00580641, version 1

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Solen Quiniou, Eric Anquetil, Sabine Carbonnel. Statistical Language Models for On-line Handwritten Sentence Recognition. International Conference on Document Analysis and Recognition, Aug 2005, Seoul, South Korea. pp.516-520. ⟨hal-00580641⟩

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