In this paper, a new off-line handwritten word recognition method based on the explicit modeling of character junctures is presented. A handwritten word is regarded as a sequence of characters and junctures of four types. Hence both characters and junctures are explicitly modeled. A handwriting system employing hidden Markov models as the main statistical framework has been developed based on this scheme. An interconnection network of character and ligature models is constructed to model words of indefinite length. This model can ideally describe any form of hamdwritten words including discretely spaced words, pure cursive words, and unconstrained words of mixed styles. Also presented are efficient encoding and decoding schemes suitable for this model. The system has shown encouraging performance with a standard USPS database.

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.