| Citation: | HAN Jingye, XU Fu, CHEN Zhibo, et al. A deep learning based interactive recognition method for telephone numbers[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 1074-1080. doi: 10.13700/j.bh.1001-5965.2017.0357(in Chinese)
|
Some sectors such as logistics, insurance and intermediary agents need to make calls frequently. Manually callings lead to low efficiency, so that telephone number recognition has important practical values. The traditional methods for printed number recognition involve complicated templates designing, which cannot meet the requirements of practical applications. An interactive method based on deep learning is proposed to recognize telephone numbers. Through double-clicking the phone number in an image, this method automatically crops the target area which contains the number and performs preprocessing operations such as grayscale, binarization, target area localization, character segmentation and image padding. An improved LeNet-5 convolutional neural network (CNN) is utilized to make image recognition, which supports the recognition of printed numbers in a variety of fonts, glyphs and font sizes. The recognition speed is optimized through multiple means such as interactive recognition and memory pool. Experimental results show that the accuracy of recognition for a single character is 99.86%, the accuracy for a telephone number is 99.50%, and the average recognition time of a telephone number is 91 ms. Comparing with the traditional methods, the new method has relatively higher accuracy and faster speed in recognition, which can be widely used in many sectors.
| [1] |
罗佳, 王玲.基于凹凸特性笔顺编码的手写体数字识别方法[J].计算机工程与科学, 2010, 29(5):69-70.
LUO J, WANG L.A new method for the off-line recognition ofhandwritten digits based on convex-concave coding[J].Computer Engineering & Science, 2010, 29(5):69-70(in Chinese).
|
| [2] |
倪桂博, 梁晓尊.基于结构形状的印刷体数字识别方法[J].软件导刊, 2010, 9(5):67-68.
NI G B, LIANG X Z.The method of printed figures based on structure[J].Software Guide, 2010, 9(5):67-68(in Chinese).
|
| [3] |
陈爱斌, 陆丽娜.基于多特征的印刷体数字识别[J].计算技术与自动化, 2011, 30(3):105-108.
CHEN A B, LU L N.The printed number character recognition based on feature[J].Computing Technology and Automation, 2011, 30(3):105-108(in Chinese).
|
| [4] |
曾志军, 孙国强.基于改进的BP网络数字字符识别[J].上海理工大学学报, 2008, 30(2):201-204.
ZENG Z J, SUN G Q.Number character recognition based on improved BP neural network[J].Journal of University of Shanghai for Science and Technology, 2008, 30(2):201-204(in Chinese).
|
| [5] |
刘春丽, 吕淑静.基于混合特征的孟加拉手写体数字识别[J].计算机工程与应用, 2007, 43(20):214-215. doi: 10.3321/j.issn:1002-8331.2007.20.063
LIU C L, LV S J.Bangla handwritten numeral recognition based on blend features[J].Computer Engineering & Applications, 2007, 43(20):214-215(in Chinese). doi: 10.3321/j.issn:1002-8331.2007.20.063
|
| [6] |
HINTON G E, SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science, 2006, 313(5786):504-507. doi: 10.1126/science.1127647
|
| [7] |
SUN Y, WANG X, TANG X. Deep learning face representation from predicting 10, 000 classes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, 2014: 1891-1898.
|
| [8] |
TAIGMAN Y, YANG M, RANZATO M, et al. DeepFace: Closing the gap to human-level performance in face verification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, 2014: 1701-1708.
|
| [9] |
SUN Y, CHEN Y, WANG X, et al. Deep learning face representation by joint identification-verification[C]//International Conference on Neural Information Processing Systems. London: MIT Press, 2014: 1988-1996.
|
| [10] |
ZHANG L, LIN L, LIANG X, et al. Is faster R-CNN doing well for pedestrian detection [C]//European Conference on Computer Vision. Berlin: Springer, 2016: 443-457.
|
| [11] |
SINGH S P, KUMAR A, DARBARI H, et al. Machine translation using deep learning: An overview[C]//International Conference on Computer, Communications and Electronics. Piscataway, NJ: IEEE Press, 2017: 162-167.
|
| [12] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, 2016: 770-778.
|
| [13] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems. London: MIT Press, 2012: 1097-1105.
|
| [14] |
CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, 2016: 3213-3223.
|
| [15] |
FARABET C, COUPRIE C, NAJMAN L, et al.Learning hierarchical features for scene labeling[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1915-1929. doi: 10.1109/TPAMI.2012.231
|
| [16] |
OTSU N.A threshold selection method from gray-level histograms[J].IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66. doi: 10.1109/TSMC.1979.4310076
|
| [17] |
LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE, 1998, 86(11):2278-2324. doi: 10.1109/5.726791
|
Figures(12) / Tables(4)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
