Character recognition system with cooperation of pattern and symbolic processing

Abstract

A newly developed character recognition method is proposed that can be applied to low quality printed documents. In this method, the cooperation of pattern processing with neural networks and symbolic processing with knowledge of language is adopted. If errors occur at one part, another part detects it and sends the error information to all parts. After successive iterations until no error is detected, a recognition result is obtained. A character recognition of 98.4 percent is obtained with this method. This rate is 2.8 percent higher than the result of a conventional method with no information exchange among processing parts.

Improving text recognition accuracy using syntax-based techniques

“…H. Niwa et al propose a post OCR method for scanned documents [37]. The correction depends on the grammar, vocabulary, and content of the documents.…”