Chinese spell checkers are more difficult to develop because of two language features: 1) there are no word boundaries, and a character may function as a word or a word morpheme; and 2) the Chinese character set contains more than ten thousand characters. The former makes it difficult for a spell checker to detect spelling errors, and the latter makes it difficult for a spell checker to construct error models. We develop a word lattice decoding model for a Chinese spell checker that addresses these difficulties. The model performs word segmentation and error correction simultaneously, thereby solving the word boundary problem. The model corrects nonword errors as well as real-word errors. In order to better estimate the error distribution of large character sets for error models, we also propose a methodology to extract spelling error samples automatically from the Google web 1T corpus. Due to the large quantity of data in the Google web 1T corpus, many spelling error samples can be extracted, better reflecting spelling error distributions in the real world. Finally, in order to improve the spell checker for real applications, we produce n-best suggestions for spelling error corrections. We test our proposed approach with the Bakeoff 2013 CSC Datasets; the results show that the proposed methods with the error model significantly outperform the performance of Chinese spell checkers that do not use error models.