Automatic metadata information extraction from scientific literature using deep neural networks

Huichen Yang; William Hsu

doi:10.1117/12.2623554

4 March 2022 Automatic metadata information extraction from scientific literature using deep neural networks

Huichen Yang, William Hsu

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 1208414 (2022) https://doi.org/10.1117/12.2623554
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

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.

Citation Download Citation

Huichen Yang and William Hsu "Automatic metadata information extraction from scientific literature using deep neural networks", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 1208414 (4 March 2022); https://doi.org/10.1117/12.2623554

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KEYWORDS

Data modeling

Neural networks

Head

Image processing

Performance modeling

Autoregressive models

Image segmentation

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