Abstract
Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.
Highlights
Ongoing demand for websites ranging from Blogs, E-commerce, Product pages has increased considerably in recent years due to the more open access to the internet
As the model identifies each feature vector from the Convolutional Neural Nets (CNN) output, the model's attention mechanism gradually shifts and focuses on the different HTML components on the same image, as shown in the Fig 4. This idea of adding an attention mechanism to the Long Short-Term Memories (LSTM) architecture was inspired by works of Kelvin Xu et al, Mahalakshmi & Sabiyath Fatima [23,20] on image captioning
3.5 Encoder While the inputted image passing through canny edge detection algorithms is fed into the CNN attached with attention mechanism in LSTM's used for object detection
Summary
Ongoing demand for websites ranging from Blogs, E-commerce, Product pages has increased considerably in recent years due to the more open access to the internet. As the model identifies each feature vector from the CNN output, the model's attention mechanism gradually shifts and focuses on the different HTML components on the same image, as shown in the Fig 4 This idea of adding an attention mechanism to the LSTM architecture was inspired by works of Kelvin Xu et al, Mahalakshmi & Sabiyath Fatima [23,20] on image captioning. 3.5 Encoder While the inputted image passing through canny edge detection algorithms is fed into the CNN attached with attention mechanism in LSTM's used for object detection. The other set of dependent LSTMs is provided with inputs from the word embedding for Automatic Code Generation (ACG) is stacked with One-hot encoders for the final output This one-hot encoding arrangement establishes a relationship in terms of digits and HTML tags. A concatenated layer is introduced to create a final set of image-HTML features
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