Matching database records to handwritten text

This paper describes a method for matching specific database records to handwritten text. While a database record contains multiple fields with complete, idealized strings, handwritten text may contain missing fields, misspellings, and abbreviations. Multiple word segmentation hypotheses are used in this method to overcome the spacing difficulties of handwritten text. To avoid the combinatorics of matching all instantiations of the record, including abbreviations and omissions, to all hypothesized word segmentations, a dynamic programming approach is employed. Inputs to the matching module include a binarized line of handwritten text and a set of potential database records. The module determines the best word segmentation, or parse, of the line given a particular record and produces an overall verification score. This module was tested using binarized, handwritten address images captured from a live mail stream. Results of matching the street line images to postal database records are presented.