Microprocessor-based recognition of handprinted characters from a tablet input

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 not 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.

A computational theory of visual word recognition

This paper presents a bibliography of over 1250 references related to the computer processing of pictorial information, arranged by subject matter. Coverage is restricted, for the most part, to a selected set of U.S. journals and proceedings of specialized meetings. The topics covered include digitization, approximation, and compression; transforms, filtering, enhancement, restoration, and reconstruction; hardware and software; pictorial pattern recognition; feature detection, segmentation, and image analysis; matching and time-varying imagery; shape and pattern; texture; formal models; and three-dimensional scene analysis. No attempt is made to evaluate or summarize the items cited; the purpose is simply to provide a convenient compendium of references.

A. Badreldin

Papers

Tree classification algorithm for handwritten character recognition.