Image data mining from financial documents based on wavelet features

In this paper, we present a framework for clustering and classifying cheque images according to their payee-line content. The features used in the clustering and classificationprocesses are extracted from the wavelet domain by means of thresholding and counting of wavelet coeilficients. The feasibilityof 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-basedtechniques. We achieved coiiect-classificationrates of 86% and 81% for the supervised and unsupervised learning cases, respectively. These rates are the average of correct-classificationrates obtained from the 10different accounts.

• Type: Conference
• Year: 2001
• Where: ICIP
• Authors: Ossama El Badawy, Mahmoud R. El-Sakka, Khaled Hassanein, Mohamed S. Kamel
• Keywords: Cheque Images, ICIP 2001, Image Processing, Unsupervised Learning Cases, Wavelet Domain