Online handwritten cursive word recognition using segmentation-free and segmentation-based methods

Abstract

This paper describes a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method. To search the optimal segmentation and recognition path as the recognition result, we attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths are evaluated by integrating character recognition scores with geometric characteristics of the character patterns in a Conditional Random Field (CRF) model. Our methods restrict the search paths from the trie lexicon of words and preceding paths during path search. We show this comparison on a publicly available dataset (IAM-OnDB).

DOI: 10.1109/ACPR.2015.7486486

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Cite this paper

@inproceedings{Zhu2015OnlineHC, title={Online handwritten cursive word recognition using segmentation-free and segmentation-based methods}, author={Bilan Zhu and Arti Shivram and Venu Govindaraju and Masaki Nakagawa}, booktitle={ACPR}, year={2015} }