Reducing Annotation Workload Using a Codebook Mapping and Its Evaluation in On-Line Handwriting
Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin · 2012
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.