Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images

Version 1 : Received: 5 July 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (17:42:24 CEST)

A peer-reviewed article of this Preprint also exists.

Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images. Appl. Sci. 2021, 11, 7610. Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images. Appl. Sci. 2021, 11, 7610.

Journal reference: Appl. Sci. 2021, 11, 7610
DOI: 10.3390/app11167610

Abstract

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

Keywords

Formula detection; Cascade Mask R-CNN; Mathematical expression detection; document image analysis; deep neural networks; computer vision.

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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