Character Recognition by Adaptive Statistical Similarity

Thomas Breuel
International Conference for Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2003
accepted for publication


Handwriting recognition and OCR systems need to cope with a wide variety of writing styles and fonts, many of them possibly not previously encountered during training. This paper describes a notion of Bayesian sta- tistical similarity and demonstrates how it can be applied to rapid adaptation to new styles. The ability to general- ize across different problem instances is illustrated in the Gaussian case, and the use of statistical similarity Gaus- sian case is shown to be related to adaptive metric classi- fication methods. The relationship to prior approaches to multitask learning, as well as variable or adaptive metric classification, and hierarchical Bayesian methods, are dis- cussed. Experimental results on character recognition from the NIST3 database are presented.




@inproceedings{ BREU2003,
	Title = {Character Recognition by Adaptive Statistical Similarity},
	Author = {Thomas Breuel},
	BookTitle = {International Conference for Document Analysis and Recognition (ICDAR)},
	Note = {accepted for publication},
	Year = {2003},
	Publisher = {IEEE Computer Society}

Last modified:: 30.08.2016