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An Integrated Algorithm for Text Recognition: Comparison with a Cascaded Algorithm
April 1983 (vol. 5 no. 4)
pp. 384-395
Jonathan J. Hull, Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226.
Sargur N. Srihari, MEMBER, IEEE, Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226.
Ramesh Choudhari, Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226; Department of Computer Science, Claflin College, Orangeburg, SC 29115.
The use of diverse knowledge sources in text recognition and in correction of letter substitution errors in words of text is considered. Three knowledge sources are defined: channel characteristics as probabilities that observed letters are corruptions of other letters, bottom-up context as letter conditional probabilities (when the previous letters of the word are known), and top-down context as a lexicon. Two algorithms, one based on integrating the knowledge sources in a single step and the other based on sequentially cascading bottom-up and top-down processes, are compared in terms of computational/storage requirements and results of experimentation.
Citation:
Jonathan J. Hull, Sargur N. Srihari, Ramesh Choudhari, "An Integrated Algorithm for Text Recognition: Comparison with a Cascaded Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 4, pp. 384-395, April 1983, doi:10.1109/TPAMI.1983.4767408
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