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A Rapid Combined Model for Automatic Generating Web UI Codes

Encoder-Decoder network is usually applied to image caption to automatically generate descriptive text for a picture. Web user interface (Web UI) is a special type of image and is usually described by HTML (hypertext marked language). Consequently, it becomes possible to use the encoder-decoder network to generate the corresponding code from a screenshot of Web UI. The basic structure of the decoder is RNN, LSTM, GRU, or other recurrent neural networks. However, this kind of decoder needs a long training time, so it increases the time complexity of training and prediction. The HTML language is a typically structured language to describe the Web UI, but it is hard to express the timing characteristics of the word sequence and the complex context. To resolve these problems efficiently, a rapid combined model RCM (rapid combined model) is designed in this paper. The basic structure of the RCM is an encoder-decoder network. The word embedding matrix and visual model are included in the encoder. The word embedding matrix uses fully connected units. Compared with LSTM, the accuracy of the word embedding matrix is basically unchanged, but the training and prediction speed have been significantly improved. In the visual model, the pretrained InceptionV3 network is used to generate the image vector, which not only improves the quality of the recognition of the Web UI interface image but also reduces the training time of the RCM significantly. In the decoder, the word embedding vector and the image vector are integrated together and input into the prediction model for word prediction.