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Multi-prototype classification: improved modelling of the variability of handwritten data using statistical clustering algorithms

Multi-prototype classification: improved modelling of the variability of handwritten data using statistical clustering algorithms

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The principal obstacle in successfully recognising handwritten data is the inherent degree of intra-class variability encountered. This calls for subclass modelling of handwritten data based on the statistically significant variations within the main classes. A novel multi-prototyping approach based on statistical clustering techniques is investigated as an appropriate solution to this problem and very encouraging results have been achieved.

Inspec keywords: pattern classification; statistical analysis; handwriting recognition; real-time systems

Other keywords: intra-class variability; multi-prototype classification; statistical clustering algorithms; variability modelling; subclass modelling; handwritten data

Subjects: Character recognition; Other topics in statistics; Computer vision and image processing techniques

References

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      • M.D. Garris , R.A. Wilkinson . (1992) NIST special database 3: Handwritten segmented characters.
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