A two-stage multi-network ocr system with a soft pre-classifier and a network selector

Abstract

We propose a generic two-stage multi-network classification scheme and a realization of this generic scheme: A two-stage multi-network OCR system. The generic two-stage multi-network classification scheme decomposes the estimation of a posteriori probabilities into two coarse-to-fine stages. This generic classification scheme is especially suitable for the classification tasks which involve a large number of categories. The two-stage multi-network OCR system consists of a bank of specialized networks, each of which is designed to recognize a subset of whole character set. A soft pre-classifier and a network selector are employed in the two-stage multi-network OCR system for selectively invoking necessary specialized networks. The network selector makes decisions based on both the prior case information and the outputs of the preclassifier. Compared with the system which uses either a single network or one-stage multiple networks, the two-stage multi-network OCR system offers advantages in recognition accuracy, confidence measure, speed, and flexibility.