Incorporating a wavelet transform into a saliency-based method for online handwriting segmentation

In the framework of a saliency-based approach for segmenting cursive handwriting into elementary strokes, we propose a smoothing technique based on the use of a Wavelet Transform to describe the electronic ink at different resolutions. According to such an approach, derived in analogy with those proposed in the literature for early visual tasks in primates, curvature maxima corresponding to actual segmentation points are separated from those produced by the different source of noise affecting the handwriting generation process. This is obtained by evaluating the curvature maxima at different levels of resolution, and by arranging them into a pyramidal structure, from which a saliency map is eventually achieved by combining the values across all possible scales. Such maps enjoy the property of exhibiting higher values in correspondence of regions of the original ink where curvature maxima survive along difference scales, thus indicating that those regions should correspond to the parts of the ink where two strokes join. Experiments performed by using both standard databases and one of online words collected in our laboratory, have shown high performance in terms of correct segmentation, stability and predictability of the results