Abstract
A novel scheme for structural modelling of multifont and handwritten characters is presented. The focus is on constructing such structural models that can be hierarchically interpreted leading to a multistage recognition scheme. This can form a basis of a high speed but reliable classifier which leaves the task of detailed discrimination between confusion classes to the secondary stage. The proposed class descriptors or prototype shape models utilise a certain “well-thought” set of shape primitives. They are “simplified” enough to ignore the inter-class variations in font-type or writing style yet retaining enough details for discrimination between the samples of the similar classes at the secondary stage. This type of modelling when combined with a good scheme of checking the spatial interrelation of features results in a powerful character recognition system utilising minimal prototypes per class. It also proves to be robust against various distortions and degradation like touching and broken characters.
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Keywords
- Secondary Stage
- Handwritten Character
- Prototype Class
- Handwritten Character Recognition
- Prototype Feature
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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© 1998 Springer-Verlag Berlin Heidelberg
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Khan, N.A., Hegt, H.A. (1998). On structural modelling for omnifont and handwritten character recognition. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033276
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DOI: https://doi.org/10.1007/BFb0033276
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