Recognition of the cursive handwritten courtesy amounts of Malaysian bank cheques.
In this paper, we present techniques to recognise the cursive handwritten courtesy amount from Malaysian bank cheques and convert it into its corresponding ASCII codes. The image is pre- processed and segmented using the combination of crossed-point, curved-point and histogram techniques. Three t...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2001
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/20832/ http://psasir.upm.edu.my/id/eprint/20832/1/ID%2020832.pdf |
| Summary: | In this paper, we present techniques to recognise the cursive handwritten courtesy amount from
Malaysian bank cheques and convert it into its corresponding ASCII codes. The image is pre-
processed and segmented using the combination of crossed-point, curved-point and histogram
techniques. Three type of feature extraction methods which are transitions, shapes and moment
invariant are used to extract local and global features of segmented handwritten digits. The
main purpose of the feature extraction is to capture the most relevant and discriminant
characteristics of the object. A three layer Neural Network architecture with the new error
function of backpropagation learning algorithm is used as recogniser. This approach yields
good recognition results with faster convergence rates. |
|---|