Document retro-conversion for personalized electronic reedition
In this paper, we propose a generic framework to store, retrieve, transform and present mixed sets of native and virtual documents. We intend to use or to develop specific tools organized in a global architecture, from document analysis and capture, document retrieval and classification-categorization, to full generation of personal sets of documents, corresponding to user's specific needs and profile. The first step concerns document preparation and formal analysis. The second step adds semantic metadata, content indexing, and structure-semantic analysis. The third step helps user for the constitution of personalized documents. Research is based on domain specific large sets of documents, as for example European Union law documents (many millions, many file formats, in twenty official languages).
Keywords: Document image analysis, neural network, OCR combination.
Year: 2005
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