Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm

A recognition system for general isolated off-line handwritten words using an approximate segment-string matching algorithm is described. The fundamental paradigm employed is a character-based segment-then-recognize/match strategy. An additional user supplied contextual information in the form of a lexicon guides a graph search to estimate the most likely word image identity. This system is designed to operate robustly in the presence of document noise, poor handwriting, and lexicon errors. A pre-processing step is initially applied to the image to remove noise artifacts and normalize the handwriting. An oversegmentation approach is used to improve the likelihood of capturing the individual characters embedded in the word. A directed graph is constructed that contains many possible interpretations of the word image, many implausible. The most likely graph path and associated confidence is computed for each lexicon word to produce a final lexicon ranking. Experiments highlighting the characteristics of this algorithm are given.