Visual keyword recognition using hidden Markov models

An algorithm for robust machine recognition of keywords embedded in a poorly printed document is presented. For each keyword, two statistical models, named hidden Markov models (HMMs), are created for representing the actual keyword and all the other extraneous words, respectively. Dynamic programming is then used for matching an unknown input word with the two models and making a maximum likelihood decision. Both the 1D and pseudo-2D HMM approaches are proposed and tested. The 2D models are shown to be general enough in characterizing printed words efficiently. These pseudo-2D HMMs facilitate an elastic matching property in both the horizontal and vertical directions, which makes the recognizer not only independent of size and slant but also tolerant of highly deformed and noisy words. The system is evaluated on a synthetically created database. Recognition accuracy of 99% is achieved when words in testing and training sets are in the same font size, and 96% is achieved when they are in different sizes. In the latter case, the 1D HMM achieves only a 70% accuracy rate