Container Code Recognition from Images with Large Perspective Deformation

doi:10.11908/j.issn.0253-374x.2019.02.018

Home > Archive>Volume 47, Issue 02, 2019 >0285-0290. DOI:10.11908/j.issn.0253-374x.2019.02.018

Container Code Recognition from Images with Large Perspective Deformation
DOI:
                        10.11908/j.issn.0253-374x.2019.02.018
                    
CSTR:
                        [cstr]
                    
Author:
                        ZHANG ShaomingZHANG Shaoming
College of Surveying and GeoInformatics, Tongji University, Shanghai 200092, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
MAO YifanMAO Yifan
College of Surveying and GeoInformatics, Tongji University, Shanghai 200092, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
WANG JianmeiWANG Jianmei
College of Surveying and GeoInformatics, Tongji University, Shanghai 200092, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
FENG TiantianFENG Tiantian
College of Surveying and GeoInformatics, Tongji University, Shanghai 200092, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:
Clc Number:TP391
Fund Project:

Article

Figures

Metrics

Reference [16]

Cited by

Materials

Comments

Abstract:

A novel method is proposed in this paper to recognize the container code from the images with large perspective deformation. First, the images are rectified by perspective transformation. Then, 26 capitalized English characters and 10 Arabic numerals are located and recognized based on the deep convolution neural network model. Finally, container codes are recognized from the candidate character set by cascade decision rules based on the priori knowledge of container code. The proposed method is verified by 1035 container images taken in Chongqing Port. The result shows that the accuracy of container code recognition reaches 97%, and the speed based on NVIDIA GeForce GTX1080 GPU is 2 to 5 frames/sec.

Key words:container code recognition; character detection; character recognition; deep convolution neural network; cascade decision rules

Reference

[1]X. Chen and A. L. Yuille. Detecting and reading text in natural scenes. CVPR, 2:366–373, 2004.

[2]L. Jung-Jin, P.-H. Lee, S.-W. Lee, A. Yuille, and C. Koch.Adaboost for text detection in natural scene. In ICDAR 2011,pages 429–434, 2011.

[3]B. Epshtein, E. Ofek, and Y. Wexler. Detecting text in natural scenes with stroke width transform. In CVPR 2010, pages 2963 –2970.

[4]Y.-F. Pan, X. Hou, and C.-L. Liu. Text localization in natural scene images based on conditional random field. In ICDAR 2009, pages 6–10. IEEE Computer Society, 2009.

[5]L. Neumann and J. Matas. A method for text localization and recognition in real-world images. In Proc. of ACCV, 2010.

[6]A. Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang, D. J. Wu, and A. Y. Ng. Text detection and character recognition in scene images with unsupervised feature learning. In Proc. of ICDAR, 2011.

[7]T. Wang, D. J. Wu, A. Coates, and A. Y. Ng. End-to-end text recognition with convolutional neural networks. In Proc. of ICPR, 2012.

[8]Y. Lecun, L. Bottou, Y. Bengio, et al. Gradient-Based Learning Applied to Document Recognition. In Proc. of the IEEE, 1998(86):11.

[9]M. Jaderberg, A. Vedaldi, and A. Zisserman. Deep features for text spotting. In Proc. of ECCV, 2014.

[10]C. Y. Lee, A. Bhardwaj, W. Di, V. Jagadeesh, and R. Piramuthu. Region-based discriminative feature pooling for scene text recognition. In Proc. of CVPR, 2014.

[11]C. Yao, X. Bai, B. Shi, and W. Liu. Strokelets: A learned multi-scale representation for scene text recognition. In Proc. of CVPR, 2014.

[12]Shaoqing Ren, Kaiming He, Ross Girshick, et al. Faster R-cnn: Towards real-time object detection with region proposal networks[J]. arXiv, 2016.

[13]Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection. arXiv:1608.08021.

[14]Matthew D.Zeiler, Rob F. Visualizing and understanding convolutional networks[J]. ArXiv, 2013.

[15]R. Girshick, “Fast R-CNN,” in Proc. IEEE Int. Conf. Comput. Vis.,2015, pp. 1440–1448.

[16]Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS. vol. 1, p. 4 (2012)

Get Citation

ZHANG Shaoming, MAO Yifan, WANG Jianmei, FENG Tiantian. Container Code Recognition from Images with Large Perspective Deformation[J].同济大学学报(自然科学版),2019,47(02):0285~0290

Copy

Article Metrics

Abstract:
PDF:
HTML:
Cited by:

History

Received:September 11,2017
Revised:October 03,2018
Adopted:November 28,2018
Online: February 28,2019
Published:

Get Citation

Share

Article Metrics

History

Article QR Code