Document Type

Conference Proceeding

Publication Date


Publication Title

International Conference on Frontiers in Handwriting Recognition


Inkball models provide a tool for matching and comparison of spatially structured markings such as handwritten characters and words. Hidden Markov models offer a framework for decoding a stream of text in terms of the most likely sequence of causal states. Prior work with HMM has relied on observation of features that are correlated with underlying characters, without modeling them directly. This paper proposes to use the results of inkball-based character matching as a feature set input directly to the HMM. Experiments indicate that this technique outperforms other tested methods at handwritten word recognition on a common benchmark when applied without normalization or text deslanting.


Image processing, Image recognition, Optical character recognition software

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


“Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.”


Author’s submitted manuscript.

inkb-hmm-poster.pdf (1844 kB)



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