This paper presents an efficient computation method for speeding up contextual postprocessing in Chinese character recognition systems. In contextual postprocessing, the words contained in the candidate character sets generated by a recognition system have to be found in order to construct a word transition graph. A language model and be used to find the most promising sentence hypothesis from the word transition graph. Finding the words contained in the candidate character sets is generally time-consuming. To accomplish the task more efficiently, the words in the dictionary are organized according to their first two characters, which form a two-character index array. Because the size of the two-character index array is much greater than the number of words in the dictionary, the two-character index array is still sparse. We compress the index array by applying a row displacement method. A compression rate of 224 can be obtained. Experimental results show that the proposed method utilizing a well-organized dictionary can find words very efficiently and that the time consumed is almost independent of the word length. Thus, our method provides a more practical technique for contextual postprocessing.