We present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. The approach is based on building a kernel function on the graph modeling the invariant manifold. It provides a way for taking into account nearly arbitrary transformations of the input samples. The approach is verified experimentally on the task of optical character recognition, providing state-of-the-art performance on harder problem settings.