InMAS: Deep Learning for Designing Intelligent Making System

Shao, Lei; Li, Maoyang; Yuan, Lianjun; Gui, Guan

doi:10.1109/access.2019.2911663

Cited by 14 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has achieved significant success in a number of industries, such as intelligent wireless communications [40], safety monitoring [41]. It has been applied to a wide range of tasks, including image and speech recognition, natural language processing, and predictive modeling [42][43] [44]. Generative adversarial networks (GANs) are a type of deep learning model that have made significant contributions to the field of machine learning in recent years.…”

Section: : Introductionmentioning

confidence: 99%

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

ullah

2023

Preprint

View full text Add to dashboard Cite

In the field of machine learning, various models are used to train and test machines to perform a variety of tasks, including data analysis, computer vision, speech recognition, pattern matching, pattern recognition, and natural language processing. Computer vision is an important area where deep learning architectures, GANs, and transfer learning are applied. In the field of pattern matching and pattern recognition, research based on deep learning models has attracted significant attention from researchers due to their excellent performance. Pashto is an ancient and historical language spoken in Pakistan and Afghanistan. Optical character recognition (OCR) systems using Generative Adversarial Network (GAN) have been developed for several cursive languages, such as Chinese, Urdu, and Japanese, but no study has been done yet to recognize Pashto handwritten characters using the GAN network architecture. In this study, we used a Generative Adversarial Network (GAN) to generate additional images of handwritten characters in order to help improve the accuracy of a recognition system for handwritten characters. We used a dataset of 43,000 images representing 43 different classes of Pashto characters, which was collected from GitHub repositories and is available for other researchers to use. We employed two deep transfer models, ResNet50 and VGGNet16, to recognize the Pashto handwritten characters and evaluated their performance. The results showed that VGGent16 achieved 98.39% testing accuracy while ResNet50 achieved 99.24% testing accuracy. From the experimental result found that ResNet50 achieved a higher accuracy rate than VGGNet16 when tested on the dataset of Pashto characters. Furthermore, the accuracy rate achieved by ResNet50 was higher than the accuracy rates reported in previous studies on Pashto character recognition.

show abstract

Section: : Introductionmentioning

confidence: 99%

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

ullah

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It is necessary to establish an intelligent financial reimbursement system to automatically complete reimbursement tasks, with low consumption of human and material resources. Recently, deep learning [1] has been greatly studied in computer vision [2], face recognition [3], image classification [4]- [6], object detection [7], [8], and water level observing [9], which have received many performance improvements. In addition, deep learning has been successfully applied in Internet of Things (IoT) [10], [11], wireless communications [12]- [15], cognitive radio [16], [16].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Aided System Design Method for Intelligent Reimbursement Robot

Gao

Ding

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Recently, the massive use of invoices has increased the burden on financial staff. In order to ensure high accuracy, conventional financial reimbursement is mainly manual, which wastes lots of human and material resources. This paper proposes an intelligent reimbursement system based on a deep learning method. The system mainly contains face recognition, invoice identification, and information storage. Face recognition ensures the security in the invoice reimbursement, and invoice identification has acceptable accuracy and operating speed. The experimental results indicate that the proposed system achieves both high accuracy and fast running speed.

show abstract

“…It introduces the residual network to set shortcut connections, to utilize multi-scale features for object detection, and to replace softmax with logistic for object classification. Deep learning has achieved great achievement in various industries, for example intelligent wireless communications [3]- [5], power amplifiers modeling [6], safety monitoring [7] and intelligent making system [8]. Recent years, generative adversarial network (GAN), has made a great contribution in the field of deep learning.…”

Section: Introductionmentioning

confidence: 99%

Attention GAN-Based Method for Designing Intelligent Making System

et al. 2019

Self Cite

View full text Add to dashboard Cite

Design of the intelligent making system (iMAS) poses a big technical challenge to recognize both printed and handwriting characters from the scanned images. In this paper, we propose an iMAS method based on the attention generative adversarial network (GAN), which innovatively applies the idea of removing raindrops through attention GAN to remove the printed text first, and then generates the image of handwriting text. The proposed method introduces a classical attention model in the visual field and generates the attention maps which focus only on the important areas through recurrent neural network (RNN), and adopts you only look once at object detection (YOLOv3) method to recognize the characters. Experimental results show that the structural similarity (SSIM) of the image generated by attention GAN is 0.89 and the accuracy of recognition is 91.34%. INDEX TERMS Attention model, generative adversarial network (GAN), recurrent neural network (RNN), object detection.

show abstract

InMAS: Deep Learning for Designing Intelligent Making System

Cited by 14 publications

References 10 publications

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

Deep Learning Aided System Design Method for Intelligent Reimbursement Robot

Attention GAN-Based Method for Designing Intelligent Making System

Contact Info

Product

Resources

About