scholarly journals DocReader: Bounding-Box Free Training of a Document Information Extraction Model

2021 ◽  
pp. 451-465
Author(s):  
Shachar Klaiman ◽  
Marius Lehne
Keyword(s):  
Information Extraction ◽  
Bounding Box ◽  
Extraction Model

scholarly journals Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Moyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Information Extraction ◽  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Urban Landscape ◽  
Kappa Coefficient ◽  
Deep Convolutional Neural Network ◽  
Landscape Information ◽  
Extraction Model

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

scholarly journals Information Extraction Model to Improve Learning Game Metadata Indexing

2020 ◽  
Vol 25 (1) ◽  
pp. 11-19
Author(s):  
Morie M. Wielfrid ◽  
Marfisi-Schottman Iza ◽  
Goore Bi Tra
Keyword(s):  
Information Extraction ◽  
Learning Game ◽  
Extraction Model

Study on the Methods of Information Extraction Based on Database Technology

2013 ◽  
Vol 427-429 ◽  
pp. 2114-2117
Author(s):  
Guo Dong Li ◽  
Feng Ceng
Keyword(s):  
Information Extraction ◽  
Extraction Method ◽  
Demand Curve ◽  
Input Sequence ◽  
Extraction Methods ◽  
Conditional Probability Distribution ◽  
Management Technology ◽  
Database Technology ◽  
Extraction Model ◽  
Information Database

Database technology as the data management technology, has become the main mean in the extraction methods of computer information management. This paper is focus on the information extraction and puts forward an inaccurate method of statistical model structure of planning database to carry on the information extraction method. For information database technology, it can record text address and cited as an example to have information extraction method. And it establishes a conditional probability distribution of the automatic extraction model of information and data, and gives input sequence, establishes the target demand curve, at last has best segmentation of the data information, to seek out information extraction path and the algorithm formula.

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