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Found 7 papers, 5 papers with code
CTE: A Dataset for Contextualized Table Extraction
We define the task of Contextualized Table Extraction (CTE), which aims to extract and define the structure of tables considering the textual context of the document.
Data augmentation on graphs for table type classification
Tables are widely used in documents because of their compact and structured representation of information.
Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents
Tables are widely used in several types of documents since they can bring important information in a structured way.
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis.
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DocEmul: a Toolkit to Generate Structured Historical Documents
We performed some experiments on two different handwritten collections using the toolkit to generate synthetic data to train a Convolutional Neural Network able to count the number of records in the real collections.
Vehicle classification based on convolutional networks applied to FM-CW radar signals
This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification.
Record Counting in Historical Handwritten Documents with Convolutional Neural Networks
In this paper, we investigate the use of Convolutional Neural Networks for counting the number of records in historical handwritten documents.
