Statistical mixture approach to automatic handwriting recognition
US5343537A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Oct 31, 1991 |
| Grant date | Aug 30, 1994 |
| Priority date | — |
| Expiry date | Oct 31, 2011 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/21
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions. Post-processing using a language model may be included.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.