This paper discusses the use of the bayesian network model for a classification problem related to the document image understanding field. Our application is focused on logical labeling in documents, which consists in assigning logical labels ro text blocks. The objective is to map a ser of logical tags, composing the document logical structure, to the physical text components. We build a bayesian network model that allows this mapping using supervised learning, and without imposing a priori constraints on the document structure. The learning straregy is based partly on genetic programming tools. A pmtorype has been implemented, and tested on tables of contents found in periodicals and magazines.
In REF , a generic probabilistic model is used for logical labelling of text blocks in documents, using BN classifiers where a prototype has been implemented and applied to periodical magazines.
Bayesian networks classifiers applied to documents
Object recognition supported by user interaction for service robots
Object recognition supported by user interaction for service robots
Computer Science