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24 March 2014 Probabilistic modeling of children's handwriting
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Proceedings Volume 9021, Document Recognition and Retrieval XXI; 902103 (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
There is little work done in the analysis of children's handwriting, which can be useful in developing automatic evaluation systems and in quantifying handwriting individuality. We consider the statistical analysis of children's handwriting in early grades. Samples of handwriting of children in Grades 2-4 who were taught the Zaner-Bloser style were considered. The commonly occurring word "and" written in cursive style as well as hand-print were extracted from extended writing. The samples were assigned feature values by human examiners using a truthing tool. The human examiners looked at how the children constructed letter formations in their writing, looking for similarities and differences from the instructions taught in the handwriting copy book. These similarities and differences were measured using a feature space distance measure. Results indicate that the handwriting develops towards more conformity with the class characteristics of the Zaner-Bloser copybook which, with practice, is the expected result. Bayesian networks were learnt from the data to enable answering various probabilistic queries, such as determining students who may continue to produce letter formations as taught during lessons in school and determining the students who will develop a different and/or variation of the those letter formations and the number of different types of letter formations.
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Mukta Puri, Sargur N. Srihari, and Lisa Hanson "Probabilistic modeling of children's handwriting", Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902103 (24 March 2014);

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