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A CCD based machine vision system for real-time text detection

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Abstract

Text detection and recognition is a hot topic in computer vision, which is considered to be the further development of the traditional optical character recognition (OCR) technology. With the rapid development of machine vision system and the wide application of deep learning algorithms, text recognition has achieved excellent performance. In contrast, detecting text block from complex natural scenes is still a challenging task. At present, many advanced natural scene text detection algorithms have been proposed, but most of them run slow due to the complexity of the detection pipeline and cannot be applied to industrial scenes. In this paper, we proposed a CCD based machine vision system for real-time text detection in invoice images. In this system, we applied optimizations from several aspects including the optical system, the hardware architecture, and the deep learning algorithm to improve the speed performance of the machine vision system. The experimental data confirms that the optimization methods can significantly improve the running speed of the machine vision system and make it meeting the real-time text detection requirements in industrial scenarios.

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Authors and Affiliations

  1. State Grid Hunan Electric Power Corporation Limited Research Institute, Changsha, 410007, China

    Shihua Zhao, Lipeng Sun & Yun Liu

  2. State Grid Hunan Electric Power Corporation Limited, Changsha, 410007, China

    Gang Li

  3. School of Optical and Electronics Information, Huazhong University of Science and Technology, Wuhan, 430074, China

    Binbing Liu

Authors
  1. Shihua Zhao
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  2. Lipeng Sun
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  3. Gang Li
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  4. Yun Liu
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  5. Binbing Liu
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Corresponding author

Correspondence to Binbing Liu.

Additional information

Shihua Zhao graduated from Chongqing University and obtained the Ph.D. degree in 2013. He is now working in State Grid Hunan Electric Power Corporation Limited Research Institute. His main research interests include high voltage technology, power transformer fault detection and diagnosis. He is the author or the co-author of several technical papers.

Lipeng Sun completed the B.S. degree in 2005 from Chongqing University, Chongqing, China. He also received his master degree in 2008 from Chongqing University, Chongqing, China. His current research interest is high voltage and insulation technology.

Gang Li completed the B.S. degree in 1996 from Xi’an Jiaotong University, Xi’an, China. He has been engaged in operation and maintenance technology of transformer over 20 years. His current research interest is high voltage and insulation technology.

Yun Liu received his master degree in 2009 from Guangxi University, Nanning, China. He received deputy senior engineer in 2013. His research interest is high voltage and insulation technology.

Binbing Liu received the bachelor degree in Optoelectronics from Huazhong University of Science and Technology, Wuhan, in 2000, and got the master degree and Ph.D. degree in Physical Electronics from the same university, in 2003 and 2013, respectively. Now, he is working at School of Optical and Electronics Information as a lecturer. His research interests include computer vision and machine learning.

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Zhao, S., Sun, L., Li, G. et al. A CCD based machine vision system for real-time text detection. Front. Optoelectron. 13, 418–424 (2020). https://doi.org/10.1007/s12200-019-0854-0

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  • DOI: https://doi.org/10.1007/s12200-019-0854-0

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