Empirical learning methods for digitized document recognition: an integrated approach to inductive generalization
- 1 January 1990
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A hybrid method of using empirical and supervised learning to acquire knowledge expressed in the form of classification rules is applied to optically scanned documents with the aim of automatic recognition and storage. An expert system devoted to classification recognizes a document as belonging to a class by its layout and the logical structure of a generic printed page. Decision rules for document classification are inferred by inductive generalization. The learning methodology combines a data analysis technique for linearly classifying with a conceptual method for generating disjunctive cover for each class of document.<>Keywords
This publication has 5 references indexed in Scilit:
- Two complementary techniques for digitized document analysisPublished by Association for Computing Machinery (ACM) ,1988
- A Step Towards Unification of Syntactic and Statistical Pattern RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1986
- A theory and methodology of inductive learningArtificial Intelligence, 1983
- Document Analysis SystemIBM Journal of Research and Development, 1982
- Pattern Classifiers and Trainable MachinesPublished by Springer Nature ,1981