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Unsupervised Handwritten Graphical Symbol Learning - Using Minimum Description Length Principle on Relational Graph

Generally, the approaches encountered in the field of handwriting recognition require the knowledge of the symbol set, and of as many as possible ground-truthed samples, so that machine learning based approaches can be implemented. In this work, we propose the discovery of the symbol set that is used in the context of a graphical language produced by on-line handwriting. We consider the case of a two-dimensional graphical language such as mathematical expression composition, where not only left to right layouts have to be considered. Firstly, we select relevant graphemes using hierarchical clustering. Secondly, we build a relational graph between the strokes defining an handwritten expression. Thirdly, we extract the lexicon which is a set of graph substructures using the minimum description length principle. For the assessment of the extracted lexicon, a hierarchical segmentation task is introduced. From the experiments we conducted, a recall rate of 84.2% is reported on the test part of our database produced by 100 writers.