Recurrent Neural Networks (RNNs) have achieved remarkable success in handling sequential data. However, they lack interpretability. Extracting Deterministic Finite Automata (DFAs) from black-box models can provide insight into their decision-making processes. This research focuses on extracting DFAs from RNNs trained on regular languages using an exact learning framework. The proposed approach employs the \(L^*\) algorithm to learn a DFA, and it demonstrates how a hyperplane-based method can be used to partition the RNN state space when evaluating equivalence queries.