Abstract
Recently, language models have gained importance in the field of information retrieval. In this paper, we propose a generic language model to improve a content-based document retrieval system. In this approach, character images are extracted, clustered, and analyzed to form high-level semantic terms using a statistical document model. This model simulates the long-term relationships between characters. Documents are then indexed according to these terms, and a query document is proposed to retrieve the relevant documents. The query document can be a single keyword, or it can be synthesized from a text string. The aim is to generate a semantic representation from low-level image pixels through pattern matching and document modeling. The conventional approach of generating semantic terms in document retrieval includes every possible symbol sequence in the feature representation. Comparatively, our approach can considerably reduce the dimensions of the feature space while producing retrieval results comparable to those of the conventional and state-of-the-art approaches.
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Notes
In this dataset, each document containing approximately 1,000 characters was used as the query image to retrieve relevant content from a total of 121,000 characters.
The Chinese Buddhist Canon (http://etext.fgs.org.tw/) is one of the largest Chinese Buddhist sutra collections in the world.
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Shiah, CY. Content-Based Document Image Retrieval Based on Document Modeling. J Intell Inf Syst 55, 287–306 (2020). https://doi.org/10.1007/s10844-020-00600-1
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DOI: https://doi.org/10.1007/s10844-020-00600-1