在车辆识别和车辆年检时，正确识别车架上金属刻印的车辆识别代号(VIN)是非常重要的环节。针对VIN序列，本文提出了一种基于神经网络的旋转VIN图片识别方法，它由VIN检测和VIN识别两部分组成。首先，在EAST算法基础上利用轻量级神经网络提取特征，并结合文本分割实现快速、准确的VIN检测；其次，将VIN识别任务作为一个序列分类问题，提出了一种新的识别VIN方法，即通过位置相关的序列分类器，预测出最终的车辆识别代号。为了验证本文的识别方法，引入了一个VIN数据集，其中包含用于检测的原始旋转VIN图像和用于识别的水平VIN图像。实验结果表明，本文方法能有效地识别车架VIN 图片，同时达到了实时性。
基于神经网络的车辆识别代号识别方法
作者单位信息
通信作者： yindong@ustc.edu.cn
出版日期：2021年1月15日
摘要
参考文献
[1] Smith R. An overview of the Tesseract OCR engine[C]// Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR 2007), 2007: 629–633.
[2] Mori S, Suen C Y, Yamamoto K. Historical review of OCR research and development[J]. Proc IEEE, 1992, 80(7): 1029–1058.
[3] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Z]. arXiv:1409.1556, 2014.
[4] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770–778.
[5] Tang Y B, Bu W, Wu X Q. Natural scene text detection based on multi-level MSER[J]. J Zhejiang Univ (Eng Sci), 2016, 50(6): 1134–1140.
唐有宝, 卜巍, 邬向前. 多层次MSER自然场景文本检测[J]. 浙江大学学报(工学版), 2016, 50(6): 1134–1140.
[6] Jiang H Y, Zhu L P, Ou Z P. Text recognition of natural scene image based on MSER and Tesseract[J]. Comput Knowl Technol, 2017, 13(33): 213–216.
蒋弘毅, 朱丽平, 欧樟鹏. 基于MSER和Tesseract的自然场景图像文字识别[J]. 电脑知识与技术, 2017, 13(33): 213–216.
[7] Zhang K Y, Shao K Y, Lu D. MSER fast skewed scene-text location algorithm[J]. J Harbin Univ Sci Technol, 2019, 24(2): 81–88.
张开玉, 邵康一, 卢迪. MSER快速自然场景倾斜文本定位算法[J]. 哈尔滨理工大学学报, 2019, 24(2): 81–88.
[8] Zhang G H, Huang K, Zhang B, et al. A natural scene text extraction method based on the maximum stable extremal region and stroke width transform[J]. J Xi’an Jiaotong Univ, 2017, 51(1): 135–140.
张国和, 黄凯, 张斌, 等. 最大稳定极值区域与笔画宽度变换的自然场景文本提取方法[J]. 西安交通大学学报, 2017, 51(1): 135–140.
[9] Nan Y, Bai R L, Li X. Application of convolutional neural network in printed code characters recognition[J]. Opto-Electron Eng, 2015, 42(4): 38–43.
南阳, 白瑞林, 李新. 卷积神经网络在喷码字符识别中的应用[J]. 光电工程, 2015, 42(4): 38–43.
[10] Wang K, Belongie S. Word spotting in the wild[C]//Proceedings of the 11th European Conference on Computer Vision, 2010, 6311: 591–604.
[11] Wang K, Babenko B, Belongie S. End-to-end scene text recognition[C]//Proceedings of 2011 International Conference on Computer Vision, 2011: 1457–1464.
[12] Tian Z, Huang W L, He T, et al. Detecting text in natural image with connectionist text proposal network[C]//Proceedings of the 14th European Conference on Computer Vision, 2016, 9912: 56–72.
[13] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.
[14] Liao M H, Shi B G, Bai X, et al. TextBoxes: a fast text detector with a single deep neural network[Z]. arXiv:1611.06779, 2016.
[15] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21–37.
[16] Tian Z, Huang W L, He T, et al. Detecting text in natural image with connectionist text proposal network[C]//Proceedings of the 14th European Conference on Computer Vision, 2016, 9912: 56–72.
[17] Liao M H, Shi B G, Bai X. TextBoxes++: a single-shot oriented scene text detector[J]. IEEE Trans Image Process, 2018, 27(8): 3676–3690.
[18] Ma J Q, Shao W Y, Ye H, et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Trans Multimed, 2018, 20(11): 3111–3122.
[19] Zhou X Y, Yao C, Wen H, et al. EAST: an efficient and accurate scene text detector[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 2642–2651.
[20] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 3431–3440.
[21] Deng D, Liu H, Li X, et al. PixelLink: detecting scene text via instance segmentation[Z]. arXiv:1801.01315, 2018.
[22] Shi B G, Bai X, Yao C. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[J]. I IEEE Trans Pattern Anal Mach Intell, 2017, 39(11): 2298–2304.
[23] Graves A, Fernández S, Gomez F, et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks[C]//Proceedings of the 23rd International Conference on Machine Learning, 2006: 369–376.
[24] Lee C Y, Osindero S. Recursive recurrent nets with attention modeling for OCR in the wild[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2231–2239.
[25] Lee C Y, Osindero S. Recursive recurrent nets with attention modeling for OCR in the wild[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2231–2239.
[26] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 936–944.
[27] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]// Proceedings of the 4th International Conference on 3D Vision (3DV), 2016: 565–571.
[28] Li X, Wang W H, Hou W B, et al. Shape robust text detection with progressive scale expansion network[Z]. arXiv:1806.02559, 2018.
[29] Liu X B, Liang D, Yan S, et al. FOTS: fast oriented text spotting with a unified network[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 5676–5685.
[30] Thakare S, Kamble A, Thengne V, et al. Document Segmentation and Language Translation Using Tesseract-OCR[C]//2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018.
[31] Shi B G, Yang M K, Wang X G, et al. ASTER: an attentional scene text recognizer with flexible rectification[J]. IEEE Trans Pattern Anal Mach Intell, 2019, 41(9): 2035–2048.
基金项目：
安徽省2018年度重点研究与开发计划项目(1804a09020049)
导出参考文献，格式为：
引用本文：
孟凡俊, 尹东. 基于神经网络的车辆识别代号识别方法[J]. 光电工程, 2021, 48(1): 200094.
