An Architecture for Efficient News Items Clustering and Retrieval Based on Language Models for a Dynamic Collection of E- Newspapers

Newspaper pages comprises of multiple individual articles divided into multiple columns. The challenging part of this task is to organize and integrate article blocks in the newspaper. This paper proposes a novel approach for Article reconstruction from newspapersincluding an aggregation of multiple sections of article and reading order recovery of each individual article.Thus,the process combines diverse information sources such as geometriclayout, semantic contentsand the sequence of article blocks are also enormouslymined in the model using the clustering algorithm to deal with thecomplex newspaper layouts.The work consisting of different sub tasks such as identification of sections of article, establishment of boundary for individual article, identifying the sequence of blocks. Furthermore subsequently the reading order of English text is used to aggregatethe blocks and retrieve an individual article from newspaper.