Microtext: The Revolutionary Inference Motor of Continuous Zoom Applied to Texts

F. Oliveira, J.B. da Silva Filho (Brazil)


Zoomable interface; Inference Motor; Recognition Patterns


The amount of text that we are required to read in the present days is amazing. One of the reasons for a poor performance of electronic text systems may result from the fact of users to avoid reading the whole text and make decisions mostly based on their headings (Fox, 1992). Aiming to minimize this problem, we have developed a computer-based tool for continuous zoom interfaces (graphic interface that allows a text to be continuously magnified of decreased). Prior to displaying the text, this tool performs a previous analysis of the text, taking into account its grammatical classification. Then, the text is displayed in its most condensed format and lowest zoom level, where only the most important words are shown. For that, the tool uses the artificial intelligence technique (pattern recognition). The text is then displayed in its most condensed format and lowest zoom level. As zoom increases, more elements of the text become visible. It should be highlighted that the tool deals with language “traps” and whenever the pattern recognizer fails to classify a certain word, it makes that word visible in its most reduced zoom format.

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