Hierarchical Character Recognition and Its Use in Handwritten Word/phrase Recognition

Off-line handwritten word/phrase recognition systems generally have monotonically cascaded architecture through several processing steps. In these architectures, the recognition engine follows a static model with a fixed feature space. Built-in resources are exhaustively used at each stage of the serial engine regardless of input complexity. However, the perception of a word is fundamentally an interactive process in human cognitive models; i.e., both conceptually-driven and data-driven processes work simultaneously and processing is recursively adapted to what is perceived in time. For optimality and efficiency, a system that achieves maximum performance with minimum processing effort is desirable, and autonomous adaptation to input is one of the solutions to the goal. A recursive computational model for handwritten character/word/phrase recognition that has some similarities to the human cognitive approach is proposed. Two concepts, (i) altering recognition action using feedback and (ii) evaluating and regulating terminating conditions actively, are introduced for dynamic and interactive recognition. A hierarchical classification method is presented with dynamic usage of hierarchical feature space that preserves the benefits of the multiresolution model. A lexicon-driven word recognizer which operates dynamically and has different degrees of classification ability is also presented. A concept of lexicon complexity derived from ”matching transform distance” is utilized as a decision metric, which measures the difficulty of the given lexicon set with respect to the classification ability of the character recognizer. Recognition, decision making and recursive updating from the closed loop architecture is designed for a recursive recognition scenario. This proposed model