A Kohonen self-organized map of velocity-based strokes

Given a segmentation of on-line handwriting in velocity-based strokes (VBSs), for each stroke a feature vector can be determined. The VBSs of handwriting samples from a large number of writers are presented to this Kohonen self-organizing map (SOM). This method is essentially a form of dimensionality reduction and feature vector quantization. The network consists of a number of 'cells', organized as a string (1D), sheet (2D), or cube (3D) etc. and starts the training with random values in the feature vector belonging to a cell. Here, we use a 2D network of 20x20 cells. From the huge training set of VBSs (typically over 50k strokes), a single stroke is selected at random and presented to the network. The nearest neighbour is searched and a region of cells (a bubble) in the network is made a little bit more similar to the input stroke. Several learning rules are possible for this update. Then the next sample stroke is drawn from the training set, and so forth. The essential trick of the Kohonen SOM is, that initially in training a large region of cells is updated, whereas at the end of training, only the vector of the best fitting cell is updated.

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A Kohonen self-organized map of velocity-based strokes

Notes:

There is a slide show of the training process of one of our VBS-based Kohonen self-organized maps, produced by Louis Vuurpijl.

Please refer to our Publications when using anything from the shown material.

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Up to the "NICI stroke-based recognizer of on-line handwriting" page

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Other interesting material:

o Handwriting Recognition and Document Analysis Conferences

o Pen & Mobile Computing

o NICI Handwriting Recognition Group home page

o UNIPEN tools

o Handwriting-related Java demos

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Copyright Lambert Schomaker (April 1, 1996)

since 1/Mar/1996