Automated forms-processing software and services
IBM Journal of Research and Development
A Generic System for Form Dropout
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-Scoring Post-Processing for Off-Line Handwritten-Character Recognition Verification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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 network. The network selector makes decisions based on both the prior case information and the outputs of the pre-classifier. 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.