Modeling documents for structure recognition using generalized N-grams

Author

Brugger, R. ; Zramdini, A. ; Ingold, R.

Author_Institution

Inst. de Inf., Fribourg Univ., Switzerland

Volume

1

fYear

1997

fDate

18-20 Aug 1997

Firstpage

56

Abstract

We present and discuss a novel approach to modeling logical structures of documents, based on a statistical representation of patterns in a document class. An efficient and error tolerant recognition heuristics adapted to the model is proposed. The statistical approach permits easily automated and incremental learning of the model. The approach has been partially evaluated on a prototype. A discussion of the results achieved by the prototype is finally made

Keywords

document image processing; image recognition; software fault tolerance; statistical analysis; trees (mathematics); document class; document modelling; error tolerant recognition heuristics; generalized N-grams; incremental learning; logical structures; statistical approach; statistical pattern representation; structure recognition; Application software; Decision trees; Error correction; Humans; Knowledge based systems; Optical character recognition software; Prototypes; Software prototyping; Text analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on

Conference_Location

Ulm

Print_ISBN

0-8186-7898-4

Type

conf

DOI

10.1109/ICDAR.1997.619813

Filename

619813