Holistic lexicon reduction for handwritten word recognition

The holistic paradigm in HWR has been applied to recognition scenarios involving small, static lexicons, such as the check amount recognition task. In this paper, we explore the possibility of using holistic information for lexicon reduction when the lexicons are large or dynamic, and training, in the traditional sense of learning decision surfaces from training samples of each class, is not viable. Two experimental lexicon reduction methods are described. The first uses perceptual features such as ascenders, descenders and length and achieves consistent reduction performance with cursive, discrete and mixed writing styles. A heuristic feature-synthesis algorithm is used to 'predict' holistic features of lexicon entries, which are matched against image features using a constrained bipartite graph matching scheme. With essentially unconstrained handwritten words, this system achieves reduction of 50% with less than 2% error. More effective reduction can be achieved if the problem can be constrained by making assumptions about the nature of input. The second classifier described operates on pure cursive script and achieves effective reduction of large lexicons of the order of 20,000 entries. Downstrokes are extracted from the contour representation of cursive words by grouping local extrema using a small set of heuristic rules. The relative heights of downstrokes are captured in a string descriptor that is syntactically matched with lexicon entries using a set of production rules. In initial tests, the system achieved high reduction (99%) at the expense of accuracy (75%).