Word Recognition from Sparse Graphs of Letter Candidates Using Wildcards and Multiple Experts

Abstract

Variability in handwriting styles suggests that many letter recognition engines cannot correctly identify some hand-written letters of poor quality at reasonable computational cost. Methods that are capable of searching the resulting sparse graph of letter candidates are therefore required. The method presented here employs ‘wildcards’ to represent missing letter candidates. Multiple experts are used to represent different aspects of handwriting. Each expert evaluates closeness of match and indicates its confidence. Explanation experts detemvne the degree to which the word alternative under consideration explains extraneous letter candidates. Schemata for normalisation and combination of scores are investigated and their performance compared. Hill-climbing yields near-optimal combination weights that outperform comparable methods on identical dynamic handwriting data.

Key Words:

• Poor quality handwriting recognition
• dynamic handwriting data
• multi-expert combination
• wildcards
• wildcard likelihood
• explanation expert
• self-confidence