Dr. William Hsu Profile

Dr. William Hsu

at Kansas State Univ

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Proceedings Article | 5 March 2022 Paper

Automatic metadata information extraction from scientific literature using deep neural networks

Huichen Yang, William Hsu

Proceedings Volume 12084, 1208414 (2022) https://doi.org/10.1117/12.2623554

KEYWORDS: Data modeling, Neural networks, Head, Performance modeling, Image processing, Process modeling, Machine learning, Image segmentation, Autoregressive models, Sensors

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We present a novel computer vision-based deep learning approach for metadata extraction as both a central component of and an ancillary aid to structured information extraction from scientific literature which has various formats. The number of scientific publications is growing rapidly, but existing methods cannot combine the techniques of layout extraction and text recognition efficiently because of the various formats used by scientific literature publishers. In this paper, we introduce an end-to-end trainable neural network for segmenting and labeling the main regions of scientific documents, while simultaneously recognizing text from the detected regions. The proposed framework combines object detection techniques based on Recurrent Convolutional Neural Network (RCNN) for scientific document layout detection with Convolutional Recurrent Neural Network (CRNN) for text recognition. We also contribute a novel data set of main region annotations for scientific literature metadata information extraction to complement the limited availability of high-quality data set. The final outputs of the network are the text content (payload) and the corresponding labels of the major regions. Our results show that our model outperforms state-of-the-field baselines.

Proceedings Article | 4 January 2021 Paper

Shape-aware generative adversarial networks for attribute transfer

Lei Luo, William Hsu, Shangxian Wang

Proceedings Volume 11605, 1160515 (2021) https://doi.org/10.1117/12.2586992

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Generative adversarial networks (GANs) have been successfully applied to transfer visual attributes in many domains, including that of human face images. This success is partly attributable to the facts that human faces have similar shapes and the positions of eyes, noses, and mouths are fixed among different people. Attribute transfer is more challenging when the source and target domain share different shapes. In this paper, we introduce a shape-aware GAN model that is able to preserve shape when transferring attributes, and propose its application to some real-world domains. Compared to other state-of-art GANs-based image-to-image translation models, the model we propose is able to generate more visually appealing results while maintaining the quality of results from transfer learning.

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