A feedback-based approach for segmenting handwritten legal amounts on bank cheques

Author

Zhou, Jun ; Suen, Ching Y. ; Liu, Ke

Author_Institution

Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

887

Lastpage

891

Abstract

The proposed feedback-based approach is implemented in two steps. In the first step, segmentation is done according to the structural features between the connected components in the legal amounts. In the second step, a feedback process is introduced to re-segment the parts that could not be identified in the first step. Then a multiple neural network classifier is used to verify the re-segmentation result. The confidence value produced by the classifier is used to determine the best segmentation points. This approach is tested on a CENPARMI database and the result indicates that the correct segmentation rate increased by 13.4% from the previous approach

Keywords

cheque processing; document image processing; feedback; handwritten character recognition; image classification; image segmentation; neural nets; CENPARMI database; bank cheques; confidence value; feedback-based approach; handwritten legal amounts; multiple neural network classifier; segmentation; structural features; Databases; Euclidean distance; Law; Legal factors; Machine intelligence; Neural networks; Neurofeedback; Pattern recognition; Testing; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7695-1263-1

Type

conf

DOI

10.1109/ICDAR.2001.953914

Filename

953914