This article proposes a new indicator for handwriting examination in text independent framework. Experiments of writer identification are addressed using only handwritten short lines instead of a whole character. After preprocessing such as binarization and contours extraction, profiles of contours of handwritten lines were decomposed with fifth scales wavelet decomposition. As the result, we obtained indicators which showed qualities of handwritten lines such as smooth or jaggy. The indicators were analysed with Principle Component Analysis (PCA), and eigen vectors were obtained. In a phase of writer identification, using Kernel Orthogonal Mutual Subspace Method (KOMSM), subspace was calculated by the eigen vectors. The result obtained through the experiments was not enough to satisfy. In future works, the proposed method will be applied to whole handwritten characters.