Abstract
In the field of artificial intelligence, target recognition is always a very concerned and challenging task. Scholars use various methods to indicate the accuracy rate of recognition. Convolutional neural network is a kind of artificial neural network, which is used in the field of image recognition widely. In this paper, we design and implement a handwritten numeral recognizer through data preprocessing, image normalization and noise reduction, and test relevance between recognition results and the parameters of convolutional neural network by experiment and comparison of different models, so as to obtain convolutional neural network model with the best effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns [C]. European Conference on Computer Vision. Springer,Berlin, Heidelberg (2000)
CHENG Guojian., YUE Qingqing.: Feasibility study of Convolutional Neural Network in image retrieval of rock slices [J]. Intell. Comput. Appl. 8(02):43–46+51 (2018)
Krizhevsky, A., Sutskever, I., Hinton G.E.: ImageNet classification with deep convolutional neural networks [C].International Conference on Neural Information Processing Systems. Curran Associates Inc (2012)
He, K., Zhang, X., Ren, S et al.: Deep residual learning forimage recognition [C].2016 IEEE Conference on ComputerVision and Pattern Recognition (CVPR). IEEE Computer Society (2016)
REN, S., HE K M., GIRSHICK, R et al.: Faster R-CNN: Towards real- time object detection with region proposal networks [J]. IEEE Trans. Pattern Anal. & Mach. Intell. 39(6):1137–1149 (2015)
He, K.M., Gkioxari, G., Dollar, P., et al.: Mask R-CNN[J]. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift[C]. Int. Conf. Int. Conf. Mach. Learning.JMLR. Org.: 448–456 (2015)
Szegedy, C.,Vanhoucke, V., Ioffe, S et al.: Rethinking the inception architecture for computer vision[EB]. eprint arXiv:1512. 00567 (2015)
Szegedy, C., Ioffe, S., Vanhoucke, V et al.: Inception-v4,Inception-ResNet and the impact of residual connections on learning[EB]. eprint arXiv: 1602. 07261 (2016)
Franois, C., Xception.: Deep learning with depthwise separable convolutions [EB]. eprint arXiv: 1610. 02357 (2016)
Li Peng.: Research on small-scale image recognition based on convolutional neural network [D].Guangxi Normal University (2019)
Acknowledgements
This paper is supported by National Natural Science Foundation of China (NSFC) under Grant No. 61572306
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Du, Z., Chen, P., Miao, H. (2023). Design and Application of Handwritten Numeral Recognizer Based on Convolutional Neural Network. In: Tsihrintzis, G.A., Wang, SJ., Lin, IC. (eds) 2021 International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications. Smart Innovation, Systems and Technologies, vol 314. Springer, Cham. https://doi.org/10.1007/978-3-031-05491-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-05491-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05490-7
Online ISBN: 978-3-031-05491-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)