Abstract
A computational model for the recognition of multifont machine-printed word images of highly variable quality is given. The model integrates three word-recognition algorithms, each of which utilizes a different form of shape and context information. The approaches are character-recognition-based, segmentation-based, and word-shape-analysis based. The model overcomes limitations of previous solutions that focus on isolated characters. In an experiment using a lexicon of 33,850 words and a test set of 1,671 highly variable word images, the algorithm achieved a correct rate of 89% at the top choice and 95% in the top ten choices.
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Ho, T.K., Hull, J.J. & Srihari, S.N. A computational model for recognition of multifont word images. Machine Vis. Apps. 5, 157–168 (1992). https://doi.org/10.1007/BF02626995
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DOI: https://doi.org/10.1007/BF02626995