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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2876-2880

Uyghur text detection in natural scene based on improved single deep neural network

Peng Yong1
Halidan·Abudureyimu1
Ding Weichao2
1. School of Electrical Engineering, Xinjiang University, Urumchi 830049, China
2. Suzhou Research Institute, Southeast University, Suzhou Jiangsu 215123, China
DOI: 10.19734/j.issn.1001-3695.2018.02.0187

Abstract

In order to overcome the difficulties of detecting the Uyghur text in natural scene, this paper improved a single deep neural network to detect Uyghur text in natural scene. The network structure combined the Uyghur feature extraction and the multi-layer features fusion text detection component. What was more, it predicted the position of Uyghur text bounding box and the confidence score of Uyghur text in an end-to-end manner. Uyghur feature extraction component used convolutional neural network to extract multi-scale and multi-level Uyghur features from natural Uyghur images. The multi-layer features fusion text detection component made use of the features extracted by the Uyghur feature extraction component to predict the position of the Uyghur text bounding boxes and the confidence of the Uyghur category. The analysis shows that Uyghur text has more special features than English and Chinese texts. For this feature, it designed a default box with multiple aspect ratios and multiple sizes and adjusted the size of some convolution kernels. Experiments on Uyghur natural scene images show that the improved single deep neural network method considers the influence of multi-scale and multi-level image features on the detection accuracy and improves the detection accuracy. The accuracy and the F value of the algorithm respectively reach 0.723 4 and 0.611 5.

Foundation Support

新疆维吾尔自治区自然科学基金资助项目(2016D01C048)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.02.0187
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Technology of Graphic & Image
Pages: 2876-2880
Serial Number: 1001-3695(2019)09-068-2876-05

Publish History

[2019-09-05] Printed Article

Cite This Article

彭勇, 哈力旦·阿布都热依木, 丁维超. 基于改进单深层神经网络的自然场景中维吾尔文检测 [J]. 计算机应用研究, 2019, 36 (9): 2876-2880. (Peng Yong, Halidan·Abudureyimu, Ding Weichao. Uyghur text detection in natural scene based on improved single deep neural network [J]. Application Research of Computers, 2019, 36 (9): 2876-2880. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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