On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a novel user-dependent method for the recognition of on-line handwritten notes. The method employs as a dissimilarity measure the ``degree of morphing'' between an input curve and a template curve. A physics-based approach substantiates the ``degree of morphing'' as a deformation energy and casts the problem as an energy minimization problem. The method operates upon key segmentation points that are provided by an appropriate segmentation algorithm. The segmentation objective is not to locate letters, but instead to locate corners and some key low curvature points (an easier task). This is part of the method's strategy to see the word as a generic on-line curve. Due to this strategy, the proposed method can handle collectively both, cursive words and hand-drawn line figures, the two key ingredients of handwritten notes. Most importantly, the proposed system achieves high recognition rates without ever resorting to statistical models.