반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

심층신경망을 이용한 PCB 부품의 인쇄문자 인식

Recognition of Characters Printed on PCB Components Using Deep Neural Networks

  • 조태훈 (한국기술교육대학교 컴퓨터공학부)
  • Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
  • 투고 : 2021.07.24
  • 심사 : 2021.09.11
  • 발행 : 2021.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

키워드

과제정보

이 논문은 2020년도 한국기술교육대학교 교수연구제 파견연구비 지원에 의하여 연구되었음.

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