scholarly journals Separating Chinese Character from Noisy Background Using GAN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bin Huang ◽  
Jiaqi Lin ◽  
Jinming Liu ◽  
Jie Chen ◽  
Jiemin Zhang ◽  
...  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Real Data ◽  
Semantic Segmentation ◽  
Chinese Characters ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Basic Network ◽  
Noisy Background

Separating printed or handwritten characters from a noisy background is valuable for many applications including test paper autoscoring. The complex structure of Chinese characters makes it difficult to obtain the goal because of easy loss of fine details and overall structure in reconstructed characters. This paper proposes a method for separating Chinese characters based on generative adversarial network (GAN). We used ESRGAN as the basic network structure and applied dilated convolution and a novel loss function that improve the quality of reconstructed characters. Four popular Chinese fonts (Hei, Song, Kai, and Imitation Song) on real data collection were tested, and the proposed design was compared with other semantic segmentation approaches. The experimental results showed that the proposed method effectively separates Chinese characters from noisy background. In particular, our methods achieve better results in terms of Intersection over Union (IoU) and optical character recognition (OCR) accuracy.

scholarly journals MB-GAN: Microbiome Simulation via Generative Adversarial Network

2019 ◽  
Author(s):  
Ruichen Rong ◽  
Shuang Jiang ◽  
Lin Xu ◽  
Guanghua Xiao ◽  
Yang Xie ◽  
...  
Keyword(s):  
Learning Community ◽  
Association Studies ◽  
Complex Structure ◽  
Simulated Data ◽  
Real Data ◽  
Original Data ◽  
Generative Adversarial Network ◽  
Methodology Development ◽  
Adversarial Network ◽  
Microbiome Data

AbstractSimulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models. To address this challenge, we designed a novel simulation framework for microbiome data using a generative adversarial network (GAN), called MB-GAN, by utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given dataset and compute simulated datasets that are indistinguishable from it. When MB-GAN was applied to a case-control microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.

scholarly journals Resolution Enhancement for Low-resolution Text Images Using Generative Adversarial Network

2018 ◽  
Vol 246 ◽  
pp. 03040
Author(s):  
Jie Kong ◽  
Congying Wang
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Rejection Rate ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Optical Character ◽  
Text Images

In recent years, although Optical Character Recognition (OCR) has made considerable progress, low-resolution text images commonly appearing in many scenarios may still cause errors in recognition. For this problem, the technique of Generative Adversarial Network in super-resolution processing is applied to enhance the resolution of low-quality text images in this study. The principle and the implementation in TensorFlow of this technique are introduced. On this basis, a system is proposed to perform the resolution enhancement and OCR for low-resolution text images. The experimental results indicate that this technique could significantly improve the accuracy, reduce the error rate and false rejection rate of low-resolution text images identification.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

scholarly journals Improving Road Semantic Segmentation Using Generative Adversarial Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 64381-64392
Author(s):  
Abolfazl Abdollahi ◽  
Biswajeet Pradhan ◽  
Gaurav Sharma ◽  
Khairul Nizam Abdul Maulud ◽  
Abdullah Alamri
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

scholarly journals JANE: Jointly Adversarial Network Embedding

2020 ◽  
Author(s):  
Liang Yang ◽  
Yuexue Wang ◽  
Junhua Gu ◽  
Chuan Wang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Link Prediction ◽  
Real Data ◽  
Semantic Space ◽  
Network Embedding ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Adversarial Network ◽  
Node Clustering ◽  
Topology Information ◽  
Embedding Methods

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).

FISS GAN: A Generative Adversarial Network for Foggy Image Semantic Segmentation

2021 ◽  
Vol 8 (8) ◽  
pp. 1428-1439
Author(s):  
Kunhua Liu ◽  
Zihao Ye ◽  
Hongyan Guo ◽  
Dongpu Cao ◽  
Long Chen ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Network ◽  
Adversarial Network

Sound Field Reconstruction in Rooms with Deep Generative Models

2021 ◽  
Vol 263 (5) ◽  
pp. 1527-1538
Author(s):  
Xenofon Karakonstantis ◽  
Efren Fernandez Grande
Keyword(s):  
Plane Waves ◽  
Sound Field ◽  
Real Data ◽  
Generative Models ◽  
Random Wave ◽  
Generative Adversarial Network ◽  
Underlying Distribution ◽  
Adversarial Network ◽  
Reconstruction Methods ◽  
Free Region

The characterization of Room Impulse Responses (RIR) over an extended region in a room by means of measurements requires dense spatial with many microphones. This can often become intractable and time consuming in practice. Well established reconstruction methods such as plane wave regression show that the sound field in a room can be reconstructed from sparsely distributed measurements. However, these reconstructions usually rely on assuming physical sparsity (i.e. few waves compose the sound field) or trait in the measured sound field, making the models less generalizable and problem specific. In this paper we introduce a method to reconstruct a sound field in an enclosure with the use of a Generative Adversarial Network (GAN), which s new variants of the data distributions that it is trained upon. The goal of the proposed GAN model is to estimate the underlying distribution of plane waves in any source free region, and map these distributions from a stochastic, latent representation. A GAN is trained on a large number of synthesized sound fields represented by a random wave field and then tested on both simulated and real data sets, of lightly damped and reverberant rooms.

Sign in / Sign up

Export Citation Format

Share Document