TableDet: An end-to-end deep learning approach for table detection and table image classification in data sheet images

Fernandes, J. Bennilo; Şimşek, Murat; Kantarcı, Burak; Khan, Shahzad

doi:10.1016/j.neucom.2021.10.023

Cited by 24 publications

(12 citation statements)

References 17 publications

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“…(1) In the production and circulation fields of an intelligent agricultural product supply chain, image detection and image classification based on deep learning can assist in decision-making for the supply chain [31]. (2) In intelligent agricultural supply chain sales, a deep-learning-based automatic commodity pricing model [32] can use a consumer selection model to predict the product market share and establish constrained nonlinear programming to determine the optimal price, thereby making the pricing more scientific and reducing the price setting. (3) In the cold chain logistics of agricultural products, the intelligent prediction of cold chain product demand is an important means for cold chain enterprises to reduce storage and transportation costs and improve market competitiveness [33].…”

Section: Deep Learning Production Forecasting In Intelligent Agricult...mentioning

confidence: 99%

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Ye¹,

Jin²,

Jiang³

et al. 2023

Computers, Materials &Amp; Continua

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Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network methods were 53.715, 6.707, 18.440, 1.580, and 1.436, respectively. Among these, the mean square error (MSE) values of the grey neural network, SVR, and LSTM neural network methods were 2.4979, 31.652, and 2.0618, respectively; and their R values were 0.99905, 0.94, and 0.94501, respectively. The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model, and the prediction results are more accurate. The prediction performance of the grey neural network was shown to be superior to that of SVR, and LSTM neural network, based on the comparison of parameters.

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Section: Deep Learning Production Forecasting In Intelligent Agricult...mentioning

confidence: 99%

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Ye¹,

Jin²,

Jiang³

et al. 2023

Computers, Materials &Amp; Continua

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“…̂ (13) where: represent Subblock number of direction pixels; express number of direction pixels; ̂is the gray level; is the limiting factor. The pixel value obtained only by mapping function transformation will cause the image to be blocky, and the bilinear difference processing for each point of the image can effectively avoid blocky.…”

Section: ) Adaptive Histogram Equalization With Limited Contrastmentioning

confidence: 99%

“…Ciga, O et al [12] proposed the method of learning to use classification labels to segment images, which realizes the style classification of painting images by annotating a few regions of interest. Fernandes, J et al [13] proposed an end-to-end depth learning method for table detection and table image classification in data table images. This method first learns basic feature representations, such as different types of edges and contours, through the convolution layer of the basic network.…”

Section: Introductionmentioning

confidence: 99%

Classification Method of Traditional Art Painting Style Based on Color Space Transformation

Zhe

2023

IJACSA

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In order to improve the accuracy and efficiency of traditional art painting style classification, a classification method of traditional art painting style based on color space transformation is proposed. This method preprocesses the traditional artistic painting style, improves the contrast of the image, makes the color and details of the image more vivid, and provides the basis for the subsequent color space conversion. After the traditional artistic painting style is stretched by automatic contrast stretching method, the color space is transformed. The purpose is to transform the image from one color space to another, so as to better extract the features of the image. Based on the traditional artistic painting style image after color space conversion, the traditional artistic painting image is balanced by the adaptive histogram equalization method with limited contrast, and an enhanced traditional artistic painting image is obtained, which further enhances the contrast of the image, makes the details in the image more prominent, and also enhances the overall visual effect of the image. Taking the enhanced traditional art painting images as input, the fuzzy Cmeans method is used to classify the traditional art painting styles, and the images are effectively divided into different categories according to the characteristics of the images. The experimental results show that this method can effectively enhance the image of traditional art paintings and effectively classify traditional art paintings with different styles, which has strong application effect.

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“…In the proposed method, it is assumed that the tables have already been extracted from document images and the bounding boxes of the table cells are known. Indeed, this is a reasonable assumption as there have been many studies achieving promising performance on table detection and table structure detection [5,10]. For many real cases, the input would be a document image, meaning that there is still a need for a table detection model and a table cell detection model as the pre-processing steps of the proposed method.…”

Section: The Impact Of Table Detection and Table Cell Detectionmentioning

confidence: 99%

Table Structure Recognition with Conditional Attention

Xiao¹,

Şimşek²,

Kantarcı³

et al. 2022

Preprint

Self Cite

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Tabular data in digital documents is widely used to express compact and important information for readers. However, it is challenging to parse tables from unstructured digital documents, such as PDFs and images, into machine-readable format because of the complexity of table structures and the missing of meta-information. Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval. In this study, we hypothesize that a complicated table structure can be represented by a graph whose vertices and edges represent the cells and association between cells, respectively. Then we define the table structure recognition problem as a cell association classification problem and propose a conditional attention network (CATT-Net). The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods on various datasets. Besides, we investigate whether the alignment of a cell bounding box or a text-focused approach has more impact on the model performance. Due to the lack of public dataset annotations based on these two approaches, we further annotate the ICDAR2013 dataset providing both types of bounding boxes, which can be a new benchmark dataset for evaluating the methods in this field. Experimental results show that the alignment of a cell bounding box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the Macro-average F1 score from 0.787 to 0.923.

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TableDet: An end-to-end deep learning approach for table detection and table image classification in data sheet images

Cited by 24 publications

References 17 publications

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Classification Method of Traditional Art Painting Style Based on Color Space Transformation

Table Structure Recognition with Conditional Attention

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