In this paper, we design a novel deep learning based hybrid system for automatic chord recognition. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand annotating time-synchronized chord labels requires professional musical skills and considerable labor. As a solution to this problem, we construct a large set of time synchronized MIDI-audio pairs, and use these data to train a Deep Residual Network (DRN) feature extractor, which can then estimate pitch class activations of real-world music audio recordings. Sequence classification and decoding are then performed with a trained Bidirectional LSTM and Conditional Random Fields (CRF) network. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, the performance is good and no less than other state-of-the-art systems. The proposed system also achieved good evaluation score in MIREX 2017 Automatic Chord Estimation task.