A computational theory of visual word recognition
Jonathan J. Hull · 1988
A computational theory of the visual recognition of words of text is developed. The theory, based on previous studies of how people read, includes three stages: hypothesis generation, hypothesis testing, and global contextual analysis. Hypothesis generation uses gross visual features, such as those that could be extracted from the peripheral presentation of a word, to provide expectations about word identity. Hypothesis testing integrates the information determined by hypothesis generation with more detailed features that are extracted from the word image. Global contextual analysis provides syntactic and semantic information that influences hypothesis testing. Algorithmic realization of the computational theory also consists of three stages. Hypothesis generation is implemented by extracting simple features from an input word and using those features to find a set of dictionary words with those features in common. Hypothesis testing uses this set of words to drive further selective image analysis that matches the input to one of the members of this set. This is done with a tree of feature tests that can be executed in several different ways to recognize an input word. Global contextual analysis is implemented with a process that uses knowledge of typical word-class transitions to improve the performance of the hypothesis testing stage. This is executable in parallel with hypothesis testing. This methodology is in sharp contrast to conventional machine reading algorithms which usually segment a word into characters and recognize the individual characters. Thus, a word decision is arrived at as a composite of character decisions. The algorithm presented here avoids the segmentation stage and does not require an exhaustive analysis of each character and thus is a character recognition algorithm. Statistical projections show the viability of all three stages of the proposed approach. Experiments with images of text show that the methodology performs well in difficult situations, such as touching and overlapping characters.