We combine complementary features based on foreground and background information in an HMM-based classifier to recognize handwritten digits. A zoning scheme based on column and row models provides a way of dividing the digit into zones without making the features size variant. This strategy allows us to avoid the digit normalization, while it provides a way of having information from specific zones of the digit. Recognition rates around 98% have been achieved using 60,000 digit samples of the NIST SD19 database.
Complementary features combined in an HMM-based system to recognize handwritten digits
2003-01-01
252559 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Complementary Features Combined in an HMM-Based System to Recognize Handwritten Digits
British Library Conference Proceedings | 2003
|Handwritten Digits Parameterisation for HMM Based Recognition
British Library Conference Proceedings | 1999
|A Novel Approach to Separate Handwritten Connected Digits
British Library Conference Proceedings | 2003
|Segmentation and Recognition of Handwritten Digits with the Use of Precedent-Based Models
British Library Online Contents | 1998
|