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

• Published in: Frontiers of Optoelectronics (Online) Vol. 13; no. 4; pp. 418 - 424
• Main Authors: Zhao, Shihua, Sun, Lipeng, Li, Gang, Liu, Yun, Liu, Binbing
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Biomedical Engineering and Bioengineering; Complexity; Computer vision; Deep learning; Electrical Engineering; Engineering; Machine learning; Machine vision; Optical character recognition; Optimization; Physics; Real time; Research Article; Vision systems; deep learning; optical character recognition (OCR); machine vision; text detection
• ISSN: 2095-2759; 2095-2767
• DOI: 10.1007/s12200-019-0854-0

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.