document: Browsing on small screens : recasting web-page segmentation into an efficient machine learning framework Fitting enough information from webpages to make browsing on small screens compelling is a challenging task . One approach is to present the user with a thumbnail image of the full web page and allow the user to simply press a single key to zoom into a region ( which may then be transcoded into wml\/xhtml , summarized , etc ) . However , if regions for zooming are presented naively , this yields a frustrating experience because of the number of coherent regions , sentences , images , and words that may be inadvertently separated . Here , we cast the web page segmentation problem into a machine learning framework , where we re-examine this task through the lens of entropy reduction and decision tree learning . This yields an efficient and effective page segmentation algorithm . We demonstrate how simple techniques from computer vision can be used to fine-tune the results . The resulting segmentation keeps coherent regions together when tested on a broad set of complex webpages .
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