Application of Statistic Properties of Letters Succession in Polish Language to Handprint Recognition
In the paper the method of handprinted word recognition is described, which combines statistical lexical language model and character classifier properties in order to improve the recognition accuracy. The statistical lexical model determines the conditional probabilities of letters succession in the language. For some letters in polish language only very small subset of successors appears with significant conditional probability. If the confidence of predecessor recognition is assessed as high then the recognition of successor can be reliably supported by utilizing probabilistic lexical properties. In contrast to many other approaches, the method is not based on lexicons, so it can be used in these cases where the exhaustive lexicon is not available or its usage is inefficient, e.g. due to great number of elements.
KeywordsWord Recognition Character Classifier Support Factor Handwriting Recognition Handwritten Text
Unable to display preview. Download preview PDF.
- 6.Sas J., Kurzynski M. (2005) Multilevel Recognition of Structured Handprinted Documents-Probabilistic Approach, Proc. Int. Conf. on Computer Recognition Systems CORES’05, Springer Verlag (in this Volume)Google Scholar
- 7.Sas J. (2004) Handwritten Laboratory Test Order Form Recognition Module For Distributed Clinic, Journ. of Medical Informatics & Technologies, Vol 8: 59–68Google Scholar
- 8.Sas J. (2004) Three-Level, Lexicon-Based Handwritten Form Recognition Method, In: Klopotek M., Tchorzewski J. (eds) Proc, of VI Int. Conf on Artificial Intelligence AI-19’2004, Vol. 1(23): 113–124Google Scholar