Title :
Image data mining from financial documents based on wavelet features
Author :
El Badawy, Ossama ; El-Sakka, Mahmoud R. ; Hassanein, Khaled ; Kamel, Mohamed S.
Author_Institution :
Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada
fDate :
6/23/1905 12:00:00 AM
Abstract :
We present a framework for clustering and classifying cheque images according to their payee-line content. The features used in the clustering and classification processes are extracted from the wavelet domain by means of thresholding and counting of wavelet coefficients. The feasibility of this framework is tested on a database of 2620 cheque images. This database consists of cheques from 10 different accounts. Each account is written by a different person. Clustering and classification are performed separately on each account using distance-based techniques. We achieved correct-classification rates of 86% and 81% for the supervised and unsupervised learning cases, respectively. These rates are the average of correct-classification rates obtained from the 10 different accounts
Keywords :
banking; cheque processing; data mining; document image processing; feature extraction; handwritten character recognition; image classification; learning (artificial intelligence); pattern clustering; unsupervised learning; wavelet transforms; cheque image classifying; cheque image clustering; distance-based techniques; feature extraction; financial documents; handwritten text; image data mining; payee-line content; supervised learning; thresholding; unsupervised learning; wavelet features; Data mining; Design engineering; Discrete wavelet transforms; Feature extraction; Image databases; Pattern recognition; Spatial databases; Systems engineering and theory; Unsupervised learning; Wavelet domain;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.959236
