The traditional methods of HMM, although highly successful in 1-D time series analysis, have not yet been successfully extended to 2-D image analysis while fully exploiting the hierarchical design and extension of HMM networks for complex structured signals. Apart from the traditional method off-line training of the Baum-Welch algorithm, we propose a new method of real time creation of word or composite character HMMs for 2-D word/character patterns. Unlike the Latin words in which letters run left-to-right, the composition of word/character components need not be linear, as in Korean Hangul and Chinese characters. The key idea lies in the character composition at the image level and the image-to-model conversion followed by redundancy reduction. Although the resulting model is not optimal, the proposed method has much greater advantage in regard to memory usage and training difficulty. In a series of experiments in character/word spotting in document images, the system recorded the hit ratios of 80% and 67% in Hangul character and word spotting respectively without language models.