Document Type
Conference Proceeding
Publication Date
10-2016
Publication Title
International Conference on Frontiers in Handwriting Recognition
Abstract
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.
Keywords
Image processing, Image recognition, Optical character recognition software
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
“Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.”
Recommended Citation
Howe, Nicholas; Fischer, Andreas; and Wicht, Baptiste, "Inkball Models as Features for Handwriting Recognition" (2016). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/133
Poster
Comments
Author’s submitted manuscript.