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Reducing AnnotationWorkload Using a Codebook Mapping and its Evaluation in On-Line Handwriting

Jinpeng Li 1, * Harold Mouchère 1 Christian Viard-Gaudin 1
* Corresponding author
1 irccyn-ivc
IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
Abstract : The training of most of the existing recognition systems requires availability of large datasets labeled at the symbol level. However, producing ground-truth datasets is a tedious work. Two repetitive tasks have to be chained. One is to select a subset of strokes that belong to the same symbol, a next step is to assign a label to this stroke group. In this paper, we discuss a framework to reduce the human workload for labeling at the symbol level a large set of documents based on any graphical language. A hierarchical clustering is used to produce a codebook with one or several strokes per symbol, which is used for a mapping on the raw handwritten data. Evaluation is proposed on two different datasets.
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Contributor : Harold Mouchère <>
Submitted on : Monday, October 1, 2012 - 12:28:10 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:08 PM
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  • HAL Id : hal-00717851, version 1



Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Reducing AnnotationWorkload Using a Codebook Mapping and its Evaluation in On-Line Handwriting. 2012 International Conference on Frontiers in Handwriting Recognition, Sep 2012, Bari, Italy. pp.1-6. ⟨hal-00717851⟩



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