  This study is focused on the qualitative and quantitative characterization of
chaotic systems with the use of symbolic description. We consider two famous
systems: Lorenz and R\"ossler models with their iconic attractors, and
demonstrate that with adequately chosen symbolic partition three measures of
complexity, such as the Shannon source entropy, the Lempel-Ziv complexity and
the Markov transition matrix, work remarkably well for characterizing the
degree of chaoticity, and precise detecting stability windows in the parameter
space. The second message of this study is to showcase the utility of symbolic
dynamics with the introduction of a fidelity test for reservoir computing for
simulating the properties of the chaos in both models' replicas. The results of
these measures are validated by the comparison approach based on
one-dimensional return maps and the complexity measures.
