Empirical design of a multi-classifier thresholding/control strategy for recognition of handwritten street names

A central task in the interpretation of handwritten US postal addresses is the off-line recognition of the street name. A lexicon of candidate street names may be extracted from a database of postal delivery points (DPF) by first locating and recognizing numeric fields such as the ZIP code and street number. The off-line handwritten word recognition (HWR) task is made difficult by the unconstrained, omni-scriptor nature of the input, and incomplete lexicons resulting from errors in processing numeric fields and intrinsic deficiencies in the DPF. In this paper, we describe an empirical approach to the design of a multi-classifier HWR Thresholding/Control module which forms part of a real-time handwritten address interpretation (HWAI) system. The decisions of two word classifiers are combined in a hierarchical manner to improve recognition and error-rejection performance, while meeting real-time requirements. The design employs logistic regression and agreement for evidence combination, and lexicon reduction for improved throughput as well as performance. The paper concludes with experimental results and directions for future research.