A multi-level perception approach to reading cursive script

摘要

Reading cursive script is the problem of transforming language from the iconic form of cursive human handwriting to one of ordinal representation. The problem involves elements of visual perception at one level of processing and those of language perception and understanding at a higher level. The problem is approached as one of multi-level perception in which a cursive script word image is transformed through a representational hierarchy. The levels are based on descriptions that use points, contours, features, letters, and words. Global control is hierarchical until an intermediate level after which it is heterarchical. A feature representation is generated bottom-up from the image using statistical dependencies between letters and features. Ratings for partially formed words are computed using a stack and a lexicon represented as a trie. Several heuristics for low- and intermediate-level processing for cursive script are introduced, including: reference-line finding using projection profile analysis, letter segmentation based on local lower contour minima and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features (e.g., middle loop, upper-zone stroke), and finding shape-oriented events. Two modes of learning are defined: initial training with user feedback and unsupervised adaptation to the writer. Experiments demonstrating the promise of the approach are described.