The number of people learning Chinese as a Foreign Language (CFL) has been booming in recent decades. The problem of spelling error correction for CFL learners increasingly is becoming important. Compared to the regular text spelling check task, more error types need to be considered in CFL cases. In this paper, we propose a unified framework for Chinese spelling correction. Instead of conventional methods, which focus on rules or statistics separately, our approach is based on extended HMM and ranker-based models, together with a rule-based model for further polishing, and a final decision-making step is adopted to decide whether to output the corrections or not. Experimental results on the test data of foreigner's Chinese essays provided by the SIGHAN 2014 bake-off illustrate the performance of our approach.