Handwritten Digits Recognition Based on Fused Convolutional Neural Network Model

doi:10.3969/j.issn.1000-3428.2017.11.030

Abstract

Abstract:

Aiming at the problem that the recognition rate of traditional handwritten digits recognition method is low,this paper proposes a Fused Convolutional Neural Network(F-CNN) model.By combining the high-level features of the Siamese Network(SN) model and Binary Convolutional Neural Network(B-CNN) model,the F-CNN model expands the size of the high-level layers and enhances the features-expression ability of deep CNN network model.In the process of network training,a kind of periodic data shuffle strategy is designed to improve the convergence rate of the F-CNN model to realize better handwritten digits recognition.Experiments results on the public MNIST dataset show that the proposed F-CNN model has 99.10% recognition rate for handwritten digits,which outperforms the SN model and the B-CNN model.

Key words: handwritten digits, fused model, Convolutional Neural Network(CNN), data scrambling strategy, convergence rate

摘要：

针对传统手写数字识别方法识别率较低的问题,提出一种融合卷积神经网络(F-CNN)模型。通过结合暹罗网络(SN)模型和二进制卷积神经网络(B-CNN)模型的高级特征,扩展网络高级层的尺寸,增强F-CNN模型的特征表达能力。在网络训练过程中,设计周期性数据打乱策略,提高F-CNN模型的收敛速度,更好地实现手写数字识别。在MNIST数据集上的实验结果表明,融合模型对于手写数字的识别准确率达到99.10%,识别性能优于SN模型和B-CNN模型。

关键词: 手写数字, 融合模型, 卷积神经网络, 数据打乱策略, 收敛速度

CLC Number:

TP391.41

CHEN Xuan,ZHU Rong,WANG Zhongyuan. Handwritten Digits Recognition Based on Fused Convolutional Neural Network Model[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2017.11.030.

陈玄,朱荣,王中元. 基于融合卷积神经网络模型的手写数字识别[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2017.11.030.

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References

参考文献［1］芮挺,沈春林,丁健,等.基于主分量分析的手写数字字符识别［J］.小型微型计算机系统,2005,26(2):289-292. ［2］黄瀚敏,汪先矩,易正俊,等.一种基于特征提取的手写字符识别技术［J］.重庆大学学报(自然科学版),2000,23(1):66-69. ［3］杜彦蕊,李珍,宋伟宏.基于特征编码的手写字符识别技术［J］.计算机工程,2004,30(5):156-158. ［4］GORGEVIKD,CAKMAKOV D.Handwritten Digit Recognition by Combining SVM Classifiers［C］//Proceedings of EUROCON’05.Washington D.C.,USA:IEEE Press,2005:21-24. ［5］洪留荣,王耀才.应用图论和基元方向信息的手写数字识别［J］.计算机工程,2006,32(3):34-36. ［6］NIU X X,SUEN C Y.A Novel Hybrid CNN-SVM Classifier for Recognizing Handwritten Digits［J］.Pattern Recognition,2012,45(4):1318-1325. ［7］BABUU R,VENKATESWARLU Y,CHINTHA A K.Handwritten Digit Recognition Using K-Nearest Neighbour Classifier［C］//Proceedings of World Congress on Computing and Communication Technologies.Washington D.C.,USA:IEEE Press,2014:60-65. ［8］DAS N,SARKAR R,BASU S,et al.A Genetic Algorithm Based Region Sampling for Selection of Local Features in Handwritten Digit Recognition Application［J］.Applied Soft Computing,2012,12(5):1592-1606. ［9］HOSSEINI-ASL E,GUHA A.Similarity-based Text Recognition by Deeply Supervised Siamese Network［C］//Proceedings of Future Technologies Conference.Washington D.C.,USA:IEEE Press,2015:1-7. ［10］AHMED E,JONES M,MARKS T K.An Improved Deep Learning Architecture for Person Re-identification［C］//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3908-3916. ［11］LECUN Y,BOTTOU L,ORR G B,et al.Efficient BackProp［M］//ORR G B,MULLER K B.Neural Networks:Tricks of the Trade.Berlin,Germany:Springer,1998:9-50. ［12］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. ［13］MONTAVON G,ORR G,MULLER K R.Neural Networks:Tricks of the Trade［M］.Berlin,Germany:Springer,1998. ［14］SRINIVAS S,BABU R V.Deep Learning in Neural Networks:An Overview［J］.Neural Networks,2015,61:85-117. ［15］SUN Z W,KONG B,IVANOV I,et al.Noble Metal Activation Layer:US8278215［P］.［2016-09-08］. ［16］KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks［C］//Proceedings of the 25th International Conference on Neural Information Processing Systems.New York,USA:ACM Press,2012:1097-1105. ［17］JIA Y,SHELHAMER E,DONAHUE J,et al.Caffe:Convolutional Architecture for Fast Feature Embedding［C］//Proceedings of the 22nd ACM International Conference on Multimedia.New York,USA:ACM Press,2014:675-678. 编辑陆燕菲

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