Ossama El Badawy, Mahmoud R. El-Sakka, Khaled Hassanein, and Mohamed S. Kamel, "Image Data Mining From Financial Documents Based On Wavelet Features", IEEE International Conference on Image Processing, ICIP'2001, Vol. 1, pp. 1078 - 1081, October 2001, Thessaloniki, Greece.


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 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.