Abstract
Recognition of text by whole word shapes generates a set of candidate words for each printed word. A Hidden Markov Model (HMM) of syntax may be used to find the most probable sequence of syntactic tags for a sentence given the sequence of candidate sets. Candidate sets are then reduced by removing all words which are not associated with the chosen tag. We show that the tagging performance of the HMM does not deteriorate despite an increasing proportion of mis-classified words. We also show that using the model significantly reduces the number of candidates.
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References
R D Boyle and R C Thomas. Interpretation of cursive script at the word level. Technical report, School of Computer Studies, University of Leeds, June 1990.
S A Dudani, K J Breeding, and R B Mcghee. Aircraft identification by moment invariants. IEEE Transactions on Computers ,26:39–45, 1977.
G D Forney. The Viterbi algorithm. Proceedings of the IEEE ,61:268–278, March 1973.
S J Hanlon and R D Boyle. Syntactic knowledge in word level text recognition. In R Beale and J Finlay, editors, Neural Networks and Pattern Recognition in Human-Computer Interaction. Ellis Horwood, 1992.
J Hull. Incorporation of a Markov Model of language syntax in a text recognition algorithm. In Symposium oh Document Analysis and Information Retrieval ,University of Nevada, Las Vegas. 16th -18th March, 1992.
J Hull. A computational theory of visual word recognition. Report number 88–07, University of NY at Buffalo, February 1988.
S Johansson, E Atwell, R Garside, and G Leech. The tagged LOB corpus. Norwegian Computing Centre for the Humanities, Bergen, 1986.
F G Keenan, L J Evett, and R J Whitrow. A large vocabulary stochastic syntax analyser for handwriting recognition. In First International Conference on Document Analysis and Recognition ,Saint-Malo, France. September 30 -October 2, 1991.
A Kundu, Y He, and P Bahl. Recognition of handwritten word : First and second order Hidden Markov Model based approach. Pattern Recognition ,22(3):283–297, 1989.
M A O’Hair and M Kabrinsky. Beyond the OCR: reading whole words as single symbols based on the two dimensional, low frequency Fourier transform. In First International Conference on Document Analysis and Recognition ,Saint-Malo, France. September 30 -October 2, 1991.
T G Rose, L J Evett, and R J Whitrow. The use of semantic information as an aid to handwriting recognition. In First International Conference on Document Analysis and Recognition ,Saint-Malo, France. September 30 October 2, 1991.
M Shridhar and A Badreldin. High accuracy character recognition algorithm using Fourier and topological descriptors. Pattern Recognition ,17:515–524, 1984.
Q Zhang and R Boyle. A new clustering algorithm with multiple runs of iterative procedures. Pattern Recognition ,24(9):835–848, 1991.
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© 1992 Springer-Verlag London Limited
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Hanlon, S., Boyle, R. (1992). Evaluating a Hidden Markov Model Of Syntax In A Text Recognition System. In: Hogg, D., Boyle, R. (eds) BMVC92. Springer, London. https://doi.org/10.1007/978-1-4471-3201-1_48
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DOI: https://doi.org/10.1007/978-1-4471-3201-1_48
Publisher Name: Springer, London
Print ISBN: 978-3-540-19777-5
Online ISBN: 978-1-4471-3201-1
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