A Manually Captured and Modified Phone Screen Image Dataset for Widget Classification on CNNs
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The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example,deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learningdatasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet,the experiments show that the dataset composed in this study is helpful for classification according to a widget'sfunctionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is thenexecuted. Finally, the research suggests the Widg-C and Widg-D datasets—a deep learning dataset for identifyingthe widgets of smart devices—and implementing them for use with representative convolutional neuralnetwork models.
Abstract
The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example,deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learningdatasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet,the experiments show that the dataset composed in this study is helpful for classification according to a widget'sfunctionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is thenexecuted. Finally, the research suggests the Widg-C and Widg-D datasets—a deep learning dataset for identifyingthe widgets of smart devices—and implementing them for use with representative convolutional neuralnetwork models.
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