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Frontiers of Optoelectronics

ISSN 2095-2759

ISSN 2095-2767(Online)

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Front. Optoelectron.    2020, Vol. 13 Issue (4) : 418-424    https://doi.org/10.1007/s12200-019-0854-0
RESEARCH ARTICLE
A CCD based machine vision system for real-time text detection
Shihua ZHAO1, Lipeng SUN1, Gang LI2, Yun LIU1, Binbing LIU3()
1. State Grid Hunan Electric Power Corporation Limited Research Institute, Changsha 410007, China
2. State Grid Hunan Electric Power Corporation Limited, Changsha 410007, China
3. School of Optical and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China
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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.
Keywords machine vision      text detection      optical character recognition (OCR)      deep learning     
Corresponding Author(s): Binbing LIU   
Online First Date: 07 August 2019    Issue Date: 31 December 2020
 Cite this article:   
Shihua ZHAO,Lipeng SUN,Gang LI, et al. A CCD based machine vision system for real-time text detection[J]. Front. Optoelectron., 2020, 13(4): 418-424.
 URL:  
https://academic.hep.com.cn/foe/EN/10.1007/s12200-019-0854-0
https://academic.hep.com.cn/foe/EN/Y2020/V13/I4/418
Fig.1  A typical CCD based machine vision system for real-time text detection and recognition
Fig.2  Natural scene text detection: find and locate the text blocks
Fig.3  Detection results of the CTPN algorithm on two invoice images
Fig.4  Time consumption of the CTPN algorithm in various hardware environments
stage time
CNN 3.4 s
LSTM +0.2 s
proposal +1.4 s
detection +0.3 s
total 5.3 s
Tab.1  Time consumption of each stage of the CTPN algorithm
Fig.5  Time consumption of the CTPN algorithm with various network architectures. (a) Test results on CPU (Intel i7); (b) test results on GPU (NVidia GTX1080)
method speed improvement accuracy degradation
hyperparameter optimization 10% yes
SIMD acceleration 50% no
GPU acceleration 2000% no
network model optimization 80% yes
optical system improvement 250% no
Tab.2  Contribution of various optimization methods to algorithm acceleration
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[2] Yu HAN, Yubin WU, Danhua CAO, Peng YUN. Defect detection on button surfaces with the weighted least-squares model[J]. Front. Optoelectron., 2017, 10(2): 151-159.
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