This paper introduces the Generalized Eigen Cooccurrence Matrix (GECM) as a new feature to des cribe complex structures like images of handwritings for palaeographic expertise. It measures the spatial de pendency between two features in the image. It generalizes t he popular grey level cooccurrence Dependencies (SGLD) which u ses the luminance for the two features. 2nd order statistic s generate high dimensional feature space which must be reduced to overcome the curse of dimensionality. Haralick have described several descriptors suited for SGLD matrices that cannot be used in Generalized Cooccurrence. In our case, the cooccurrence matrices are not always symmetric and the contents of each matrice are different from the SGLD. We introduce t he GECM which uses the eigen decomposition of the cooccurrence matrices to reduce the number of matrices and decrease the redundancy of spatial information instead to reduce the size of each matrix. We show the effectiveness of the GECM on palaeography application and writing comparison.