Abstract
An approach to handprinted word recognition is described. The approach is based on the use of generating multiple possible segmentations of a word image into characters and matching these segmentations to a lexicon of candidate strings. The segmentation process uses a combination of connected component analysis and distance transform-based, connected character splitting. Neural networks are used to assign character confidence values to potential character within word images. Experimental results are provided for both character and word recognition modules on data extracted from the NIST handprinted character database.
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Gader, P., Whalen, M., Ganzberger, M. et al. Handprinted word recognition on a NIST data set. Machine Vis. Apps. 8, 31–40 (1995). https://doi.org/10.1007/BF01213636
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DOI: https://doi.org/10.1007/BF01213636