Intelligent generation and fine tuning of style based on the historical excellent layouts of digital newspapers
In traditional newspaper printing industry,designers need to manually typeset according to the design rules.The typesetting process is costly,time-consuming and labor-consuming.In order to improve the efficiency of typeset-ting,a method of automatic style generation and fine-tuning based on historical excellent layouts is proposed.In order to learn the style of newspaper typesetting from the data,an electronic newspaper database containing rich parameter in-formation of design elements is created,which can effectively reflect the layout of newspapers.For a given news article,firstly,a probability model is trained according to the historical excellent layout to infer the layout style of the electronic newspaper,and the fixed constraints and user constraints are combined to ensure that the style is effective.At the same time,some aesthetic design principles are quantified to further realize style fine-tuning.Finally,through qualitative and quantitative evaluation,it shows that the new method can generate newspapers that meet the visual aesthetics,hierarchy and readability.This method can provide a reference for intelligent generation of layout design styles.
万方数据
维普
