Deep Convolutional Neural Networks for Text Localisation in Figures From Biomedical Literature

Text contained within figures is an important source of information in biomedical literature. Despite this, end-to-end text extraction from biomedical figures remains a challenging task. This paper presents a novel approach to address the founding block of this task, text detection, not only from bi...

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Bibliographic Details
Published in2019 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 5
Main Authors Almakky, Ibrahim, Palade, Vasile, Ruiz-Garcia, Ariel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
Biological system modeling
biomedical figures
Biomedical imaging
Convolutional neural networks
deep learning
Image color analysis
Image reconstruction
text localisation
Training
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Summary:Text contained within figures is an important source of information in biomedical literature. Despite this, end-to-end text extraction from biomedical figures remains a challenging task. This paper presents a novel approach to address the founding block of this task, text detection, not only from biomedical figures but also from images in general. Particularly, the paper proposes an approach that simplifies the text detection problem into a reconstruction problem using a deep convolutional neural network. Designed to overcome the specific challenges of text detection from biomedical figures, our proposed model reports promising results on the DETEXT dataset.
ISSN:2161-4407
DOI:10.1109/IJCNN.2019.8852353