RCANet: A Rows and Columns Aggregated Network for Table Structure Recognition

Shen, Xinyi; Kong, Lingjun; Bao, Yunchao; Zhou, Yaowei; Li, Weiguang

doi:10.1109/ictc55111.2022.9778621

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…The research in this paper is based on the RCANet [19] model, the main structure of RCANet is shown in Figure 1. RCANet is mainly composed of three main parts, namely, the ResNet18 backbone network, rows aggregated (RA) module, and columns aggregated (CA) module.…”

Section: Rcanet Modelmentioning

confidence: 99%

“…Siddiqui et al [18] reduced table structure recognition to the prediction of table columns and table rows. Shen et al [19] designed a semantic segmentation network for the problem of high fault tolerance of rows and columns, and added feature slicing and tiling operations to the rows aggregated (RA) module and the columns aggregated (CA) module, segmenting the rows and columns of the table. Reference [20] proposed a transformer-based method for table structure identification (TableFormer), which achieved better results in predicting the table structure and bounding boxes of cells.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use a more lightweight network. The backbone network ResNet18 of the RCANet [19] model is replaced by a lightweight ShuffleNetv2 network, which greatly reduces the volume and parameters of the model; 2.…”

Section: Introductionmentioning

confidence: 99%

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Table Structure Recognition Method Based on Lightweight Network and Channel Attention

Zhang

Sui

et al. 2023

Electronics

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The table recognition model rows and columns aggregated network (RCANet) uses a semantic segmentation approach to recognize table structure, and achieves better performance in table row and column segmentation. However, this model uses ResNet18 as the backbone network, and the model has 11.35 million parameters and a volume of 45.5 M, which is inconvenient to deploy to lightweight servers or mobile terminals. Therefore, from the perspective of model compression, this paper proposes the lightweight rows and columns attention aggregated network (LRCAANet), which uses the lightweight network ShuffleNetv2 to replace the original RCANet backbone network ResNet18 to simplify the model size. Considering that the lightweight network reduces the number of feature channels, it has a certain impact on the performance of the model. In order to strengthen the learning between feature channels, the rows attention aggregated (RAA) module and the columns attention aggregated (CAA) module are proposed. The RAA module and the CAA module add the squeeze and excitation (SE) module to the original row and column aggregated modules, respectively. Adding the SE module means the model can learn the correlation between channels and improve the prediction effect of the lightweight model. The experimental results show that our method greatly reduces the model parameters and model volume while ensuring low-performance loss. In the end, the average F1 score of our model is only 1.77% lower than the original model, the parameters are only 0.17 million, and the volume is only 0.8 M. Compared with the original model, the parameter amount and volume are reduced by more than 95%.

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Section: Rcanet Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Table Structure Recognition Method Based on Lightweight Network and Channel Attention

Zhang

Sui

et al. 2023

Electronics

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“…X Shen [122] suggested two modules, referred to as Rows Aggregated (RA) and Columns Aggregated (CA). First, to produce a rough forecast for the rows and columns and address the issue of high error tolerance, feature slicing and tiling are applied.…”

Section: Table Structure Recognition Modelsmentioning

confidence: 99%

Deep learning for table detection and structure recognition: A survey

Kasem¹,

Abdallah²,

Berendeyev³

et al. 2022

Preprint

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Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/ table-detection-structure-recognition.

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