Assessing the Relationship Between Binarization and OCR in the Context of Deep Learning-Based ID Document Analysis

2021 ◽  
pp. 134-144
Author(s):  
Rubén Sánchez-Rivero ◽  
Pavel Bezmaternykh ◽  
Annette Morales-González ◽  
Francisco José Silva-Mata ◽  
Konstantin Bulatov
Keyword(s):  
Deep Learning ◽  
Document Analysis ◽  
The Relationship

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

A Deep Learning Approach for Text Segmentation in Document Analysis

2020 ◽  
Author(s):  
Van-Linh Pham ◽  
Xuan-Phung Pham ◽  
Hoai-Nam Tran ◽  
Sy-Tuyen Ho ◽  
Vinh-Loi Ly ◽  
...  
Keyword(s):  
Deep Learning ◽  
Document Analysis ◽  
Text Segmentation ◽  
Learning Approach

scholarly journals Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 800
Author(s):  
Jongchan Park ◽  
Min-Hyun Kim ◽  
Dong-Geol Choi
Keyword(s):  
Deep Learning ◽  
Simple Technique ◽  
Detection Method ◽  
Fraud Detection ◽  
Complementary Information ◽  
Multiple Modalities ◽  
Vending Machines ◽  
Emotion Intensity ◽  
The Relationship ◽  
Extract Information

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.

Deep Learning and Online Education as an Informal Learning Process

2020 ◽  
pp. 1323-1343
Author(s):  
Theresa Neimann ◽  
Victor C. X. Wang
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Informal Learning ◽  
Traditional Classroom ◽  
Student Centered ◽  
Digital Platforms ◽  
Brick And Mortar ◽  
Controversial Topic ◽  
Electronic Education ◽  
The Relationship

Informal learning is a universal current phenomenon of learning via participation, experience, or learning via student-centered knowledge creation. It stands in stark contrast with the traditional view of didactic teacher-centered learning. Online education can be regarded as a positive and self-directed form of informal learning. Whether or not deep learning takes place for the online learner is a controversial topic for many educators. This chapter will discuss the benefits and challenges of the relationship between informal online learning leading to deeper learning. But, what isn't controversial is that in this century more education has been delivered in digital platforms than in any other time in history. For most providers of education to remain highly competitive, they must engage in electronic education of some form by moving beyond the brick and mortar of the traditional classroom. Informal learning has become the impetus resulting in the extensive and intensive application of electronic education.

scholarly journals Target identification among known drugs by deep learning from heterogeneous networks

2020 ◽  
Vol 11 (7) ◽  
pp. 1775-1797 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Siyi Zhu ◽  
Weiqiang Lu ◽  
Zehui Liu ◽  
Jin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Target Identification ◽  
Drug Repurposing ◽  
Disease Network ◽  
The Relationship

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

Socializing accounting practices in governing boards: dairy co-operatives down-under

2016 ◽  
Vol 12 (1) ◽  
pp. 75-102 ◽  
Author(s):  
Karen VanPeursem ◽  
Kevin Old ◽  
Stuart Locke
Keyword(s):  
Chief Executive Officers ◽  
Design Methodology ◽  
Chief Executive ◽  
Document Analysis ◽  
Content Type ◽  
Challenges And Opportunities ◽  
Small Pool ◽  
Executive Officers ◽  
Accountability Practices ◽  
The Relationship

Purpose – The purpose of this paper is to evaluate the accountability practices of the directors in New Zealand and Australian dairy co-operatives. An interpretation of their practices, which focus on the relationship between directors and their farmer-shareholders, is informed by Roberts’ (2001a) understandings of a socializing accountability. Design/methodology/approach – The fieldwork consists of interviews with 23 directors, including all chief executive officers and chairmen, of six dairy co-operatives together with observations and document analysis. These co-operatives together comprise a significant portion of the regional dairy industry. The methodology draws from Eisenhardt’s (1989) qualitative approach to theory formation. Findings – The authors find that these directors engage in a discourse-based, community-grounded and egalitarian form of socializing accountability. As such, their practices adhere generally to Roberts (2001a) hopes for a more considerate and humble relationship between an accountor and an accountee. Social implications – Findings add to the small pool of research on the lived experiences of co-operative boards and to a parsimonious literature in socializing accountability practices. The contributions of the study are in advancing real understandings of alternative forms of accountability, in evaluating the conditions in which these alternatives may be likely to arise and in anticipating the challenges and opportunities that arise therefrom. Originality/value – The originality of the project arises from accessing the views of these industry leaders and, through their frank expressions, coming to understand how they achieve a form of a socializing accountability in their relationships with farmer-shareholders.

scholarly journals Deep learning of dynamic sea-level variability to investigate the relationship with the floods in Gothenburg

2021 ◽  
Author(s):  
Omid Memarian Sorkhabi
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Surface ◽  
Sea Level Variability ◽  
Annual Increase ◽  
The Relationship ◽  
Dynamic Sea Level ◽  
And Sea Level

Abstract It is important to study the relationship between floods and sea-level rise due to climate change. In this research, dynamic sea-level variability with deep learning has been investigated. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation and sea-level rise from satellite altimetry investigated for dynamic sea-level variability. An annual increase of 0.1 ° C SST is observed around the Gutenberg coast. Also in the middle of the North Sea, an annual increase of about 0.2 ° C is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation of SST and SSH near the Gothenburg coast. In the next step dynamic sea-level variability is predicted with long short time memory. Root mean square error of wind speed, precipitation, and mean sea-level forecasts are 0.84 m/s, 48 mm and 2.4 mm. The annual trends resulting from 5-year periods, show a significant increase from 28 mm to 46 mm per year in the last 5 year periods. The rate of increase has doubled. The wavelet can be useful for detecting dynamic sea-level variability.

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