The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
On the Recognition of Printed Characters of Any Font and Size
Abstract
We describe the current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet. The system combines several techniques in order to improve the overall recognition rate. Thinning and shape extraction are performed directly on a graph of the run-length encoding of a binary image. The resulting strokes and other shapes are mapped, using a shape-clustering approach, into binary features which are then fed into a statistical Bayesian classifier. Large-scale trials have shown better than 97 percent top choice correct performance on mixtures of six dissimilar fonts, and over 99 percent on most single fonts, over a range of point sizes. Certain remaining confusion classes are disambiguated through contour analysis, and characters suspected of being merged are broken and reclassified. Finally, layout and linguistic context are applied. The results are illustrated by sample pages.
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Cites
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Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,371
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Data Structures for Range Searching
Bentley, J. L., & Friedman, J. H. (1979) · ACM Computing Surveys · doi:10.1145/356789.356797 · Cited by 622
Automatic recognition of handprinted characters—The state of the art
Suen, C. Y., Berthod, M., & Mori, S. (1980) · Proceedings of the IEEE · doi:10.1109/proc.1980.11675 · Cited by 286
A vectorizer and feature extractor for document recognition
Pavlidis, T. (1986) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(86)90128-3 · Cited by 153
Self-citationFeature identification for hybrid structural/statistical pattern classification
Baird, H. S. (1988) · Computer Vision Graphics and Image Processing · doi:10.1016/s0734-189x(88)80042-2 · Cited by 51
Self-citationRecognition of handprinted characters by an outermost point method
Yamamoto, K., & Mori, S. (1980) · Pattern Recognition · doi:10.1016/0031-3203(80)90062-x · Cited by 46
29 Optical character recognition—Theory and practice
Nagy, G. (1982) · Handbook of statistics · doi:10.1016/s0169-7161(82)02032-x · Cited by 43
Skeletons: A link between theoretical and physical letter descriptions
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Cited by
Techniques for automatically correcting words in text
Kukich, K. (1992) · ACM Computing Surveys · doi:10.1145/146370.146380 · Cited by 1,257
Word association norms, mutual information, and lexicography
Church, K. W., & Hanks, P. (1989) · Proceedings of the 27th annual meeting on Association for Computational Linguistics - · doi:10.3115/981623.981633 · Cited by 1,094
Historical review of OCR research and development
Mori, S., Suen, C. Y., & Yamamoto, K. (1992) · Proceedings of the IEEE · doi:10.1109/5.156468 · Cited by 892
Multiresolution neural networks for omnifont character recognition
Wang, J., & Jean, J. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298793 · Cited by 11
Metadata
- Authors
- Kahan, S., Pavlidis, T., & Baird, H. S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-03
- Volume
- PAMI-9
- Issue
- 2
- Pages
- 274-288
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2155818555
- Cited by
- 339
- References
- 10
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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