Automatic speech recognition (ASR) has recently achieved remarkable success and reached human parity, thanks to the synergistic breakthroughs in neural model architectures and training algorithms. However, the performance of ASR in many real-world use cases is still far from perfect. There has been a surge of research interest in designing and developing feasible post-processing modules to improve recognition performance by refining ASR output sentences, which fall roughly into two categories. The first category of methods is ASR N-best hypothesis reranking. ASR N-best hypothesis reranking aims to find the oracle hypothesis with the lowest word error rate from a given N-best hypothesis list. The other category of methods take inspiration from, for example, Chinese spelling correction (CSC) or English spelling correction (ESC), seeking to detect and correct text-level errors of ASR output sentences. In this paper, we attempt to integrate the above two methods into the ASR error correction (AEC) module and explore the impact of different kinds of features on AEC. Empirical experiments on the widely-used AISHELL-l dataset show that our proposed method can significantly reduce the word error rate (WER) of the baseline ASR transcripts in relation to some top-of-line AEC methods, thereby demonstrating its effectiveness and practical feasibility.