결합 신경망을 이용한 여권 MRZ 정보 인식
Recognition of Passport MRZ Information Using Combined Neural Networks
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참고문헌
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초록
In case of reading passport using a smart phone in contrast with a dedicated passport reading system, MRZ(Machine Readable Zone) character recognition can be hard when the character strokes were broken, touched or blurred according to the lighting condition, and the position and size of MRZ character lines were varied due to the camera distance and angle. In this paper, the effective recognition algorithm of the passport MRZ information using a combined neural network recognizer of CNN(Convolutional Neural Network) and ANN( Artificial Neural Network), is proposed under the various sized and skewed passport images. The MRZ line detection using connected component analysis algorithm and the skew correction using perspective transform algorithm are also designed in order to achieve effective character segmentation results. Each of the MRZ field recognition results is verified by using five check digits for deciding whether retrying the recognition process of passport MRZ information or not. After we implement the proposed recognition algorithm of passport MRZ information, the excellent recognition performance of the passport MRZ information was obtained in the experimental results for PC off-line mode and smart phone on-line mode.
Abstract
In case of reading passport using a smart phone in contrast with a dedicated passport reading system, MRZ(Machine Readable Zone) character recognition can be hard when the character strokes were broken, touched or blurred according to the lighting condition, and the position and size of MRZ character lines were varied due to the camera distance and angle. In this paper, the effective recognition algorithm of the passport MRZ information using a combined neural network recognizer of CNN(Convolutional Neural Network) and ANN( Artificial Neural Network), is proposed under the various sized and skewed passport images. The MRZ line detection using connected component analysis algorithm and the skew correction using perspective transform algorithm are also designed in order to achieve effective character segmentation results. Each of the MRZ field recognition results is verified by using five check digits for deciding whether retrying the recognition process of passport MRZ information or not. After we implement the proposed recognition algorithm of passport MRZ information, the excellent recognition performance of the passport MRZ information was obtained in the experimental results for PC off-line mode and smart phone on-line mode.
참고문헌 (13)
- K.B. Kim, "A passport recognition and face verification using enhanced fuzzy ART based RBF network and PCA algorithm", Neurocomputing, 71, pp.3202~3210 (2008)
- Y.B. Kwon, "Recognition based verification for the machine readable travel documents", Int‘l Workshop on Graphics Recognition (2007)
- H.J. Lee, "Character Recognition for the Machine Readable Zone of Electronic Identity Cards", Int'l Conf. on Image Processing, pp.387~391 (2015)
- Y. Amirgaliyev, "Pattern recognition systems in the problems of automatic person identification using the passport data", Computer Modelling & New Technologies, 19, pp.27~30 (2015)
- ICAO, "Machine Readable Travel Documents", ~ (2015)
- A. Hartl, "Real-time detection and recognition of machine-readable zones with mobile devices", Proc. of the Int'l Conf. on Computer Vision Theory and Applications (2016)
- O. Petrova, "Methods of machine-readable zone recognition results post-processing", Int'l Conf. on Machine Vision (2018)
- G. Sarker, "A Convolutional Neural Network for Optical Character Recognition and Subsequent Machine Translation", Int'l J. of Computer Application, 182(30), pp.23~27 (2018)
- T. Liu, "Implementation of training convolutional neural networks", arXiv:1506.01195, ~ (2015)
- 임상헌, "딥 러닝 기반의 악성흑색종 분류를 위한 컴퓨터 보조진단 알고리즘", (사)디지털산업정보학회 논문지, 14(4), pp.69~77 (2018), (사)디지털산업정보학회[DOI]
- 최병관, "인공지능 객체인식에 관한 파라미터 측정 연구", (사)디지털산업정보학회 논문지, 15(3), pp.15~28 (2019), (사)디지털산업정보학회
- B. Gato, "Adaptive Degraded Document Image Binarization", Pattern Recognition, 39, pp.317~327 (2006)
- J. Wen, "A New Binarization Method for Non-uniform Illuminated Document Images", Pattern Recognition, 46, pp.1670~1690 (2013)