Word Extraction Associated with a Confidence Index for On-Line Handwritten Sentence Recognition
Résumé
This paper presents a word extraction approach based on the use of a confidence index to limit the total number of segmentation hypotheses in order to further extend our on-line sentence recognition system to perform on-the-fly recognition. Our initial word extraction task is based on the characterization of the gap between each couple of consecutive strokes from the on-line signal of the handwritten sentence. A confidence index is associated to the gap classification result in order to evaluate its reliability. A reconsideration process is then performed to create additional segmentation hypotheses to ensure the presence of the correct segmentation among the hypotheses. In this process, we control the total number of segmentation hypotheses to limit the complexity of the recognition process and thus the execution time. This approach is evaluated on a test set of 425 English sentences written by 17 writers, using different metrics to analyze the impact of the word extraction task on the whole sentence recognition system's performances. The word extraction task using the best reconsideration strategy achieves a 97.94% word extraction rate and a 84.85% word recognition rate which represents a 33.1% word error rate decrease relatively to the initial word extraction task (with no segmentation hypothesis reconsideration).
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