LBDA: A NOVEL FRAMEWORK FOR EXTRACTING CONTENT FROM WEB PAGES

The internet presents an enormous amount of useful information which is usually formatted for web users, but it is a complex task to extract the relevant data from various web sources. Recently, many approaches for data extraction from web pages have been proposed and each having their own merits and limitations. This paper provides a simple but effective approach, named layout based detachment approach (LBDA). The proposed approach extracts the main content from the web page and removes the irrelevant information like header, footer contents, navigation bars, advertisements and other noisy images. The proposed methodology uses the following techniques: tag tree parsing to get the analysis structure, block acquiring page segmentation method to remove unwanted tags, and data extraction to retrieve the necessary contents. It can eliminate noise and extract the main content blocks from web page effectively and display the essential content to the users. The performance is evaluated based on the following metrics like precision, recall, accuracy, execution time and memory usage. The implementation results obviously show that our proposed LBDA approach is performed better than the existing heuristic approach.