Automatic Creative Design of Digital Posters Using Spiralize Elite Learning Strategy Based Tasmanian Devil Optimization Algorithm

Traditional creative design to be time-consuming and often requires a professional designer. An Automatic Digital Poster Designer based generator automate much of this process, reducing the time and effort needed to produce high-quality posters. In this paper aims to design the automatic creative design model by using the Spiralize Elite Learning based Tasmanian Devil Optimization (SEL-TDO) and Inference algorithm to make a digital graphic poster. Initially, the dataset images are fed to the feature extraction phase in that, to extract the multi-model features such as textual and visual features by using the encoder and decoder model. After that feature extraction, in feature vector have some irrelevant feature, its lead to fall the model in overfitting issue. Instead of need to select the relevant features from the vector by using the SEL-TDO. Then finally the Inference algorithm is used to done the high quality automated graphic poster. The proposed method compares to the various metrices such as accuracy of 0.456 precision of 0.378 and recall of 0.324. This model producing the better accuracy compared to the Automatic Poster Design (AuPoD-)varieties model.