Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, A.M.Z. CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution. J. Imaging2021, 7, 214.
Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, A.M.Z. CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution. J. Imaging 2021, 7, 214.
Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, A.M.Z. CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution. J. Imaging2021, 7, 214.
Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, A.M.Z. CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution. J. Imaging 2021, 7, 214.
Abstract
Table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursive Feature Pyramid network and Switchable Atrous Convolution in the existing backbone architecture. By utilizing a comparatively lightweight backbone of ResNet-50, this paper demonstrates that superior results are attainable without relying on pre and post-processing methods, heavier backbone networks (ResNet-101, ResNeXt-152), and memory-intensive deformable convolutions. We evaluate the proposed approach on five different publicly available table detection datasets. Our CasTabDetectoRS outperforms the previous state-of-the-art results on four datasets (ICDAR-19, TableBank, UNLV, and Marmot) and accomplishes comparable results on ICDAR-17 POD. Upon comparing with previous state-of-the-art results, we obtain a significant relative error reduction of 56.36%, 20%, 4.5%, and 3.5% on the datasets of ICDAR-19, TableBank, UNLV, and Marmot, respectively. Furthermore, this paper sets a new benchmark by performing exhaustive cross-datasets evaluations to exhibit the generalization capabilities of the proposed method.
Computer Science and Mathematics, Computer Vision and Graphics
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