激光与光电子学进展, 2019, 56 (7): 072001, 网络出版: 2019-07-30   

基于多特征卷积神经网络的手写公式符号识别 下载: 1693次

Handwritten Formula Symbol Recognition Based on Multi-Feature Convolutional Neural Network
作者单位
华侨大学信息科学与工程学院厦门市移动多媒体通信重点实验室, 福建 厦门 361021
引用该论文

方定邦, 冯桂, 曹海燕, 杨恒杰, 韩雪, 易银城. 基于多特征卷积神经网络的手写公式符号识别[J]. 激光与光电子学进展, 2019, 56(7): 072001.

Dingbang Fang, Gui Feng, Haiyan Cao, Hengjie Yang, Xue Han, Yincheng Yi. Handwritten Formula Symbol Recognition Based on Multi-Feature Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(7): 072001.

参考文献

[1] BlosteinD, GrbavecA. Recognition of mathematical notation[M]. Singapore: World Scientific, 1997: 557- 582.

[2] Chan K F, Yeung D Y. Mathematical expression recognition: a survey[J]. International Journal on Document Analysis and Reconition, 2000, 3(1): 3-15.

[3] ÁlvaroF, Sánchez JA, Benedí JM. Offline features for classifying handwritten math symbols with recurrent neural networks[C]∥International Conference on Pattern Recognition, 2014: 2944- 2949.

[4] DavilaK, LudiS, ZanibbiR. Usingoff-line features and synthetic data for on-line handwritten math symbol recognition[C]∥International Conference on Frontiers in Handwriting Recognition, 2014: 323- 328.

[5] Nguyen H D, Duc le A. E99[J]. Nakagawa M. Recognition of online handwritten math symbols using deep neural networks. IEICE Transactions on Information, Systems, 2016, D(12): 3110-3118.

[6] MouchèreH, Viard-GaudinC, ZanibbiR, et al. ICFHR2016 CROHME: competition on recognition of online handwritten mathematical expressions[C]∥International Conference on Frontiers in Handwriting Recognition, 2016: 607- 612.

[7] Dong LF, Liu HC. Recognition of offline handwritten mathematical symbols using convolutional neural networks[M]. Cham: Springer International Publishing, 2017: 149- 161.

[8] RamadhanI, PurnamaB, Faraby SA. Convolutional neural networks applied to handwritten mathematical symbols classification[C]∥International Conference on Information and Communication Technology, 2016: 1- 4.

[9] 冯小雨, 梅卫, 胡大帅. 基于改进Faster R-CNN的空中目标检测[J]. 光学学报, 2018, 38(6): 0615004.

    Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 2018, 38(6): 0615004.

[10] 郭呈呈, 于凤芹, 陈莹. 基于卷积神经网络特征和改进超像素匹配的图像语义分割[J]. 激光与光电子学进展, 2018, 55(8): 081005.

    Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081005.

[11] 王民, 刘可心, 刘利, 等. 基于优化卷积神经网络的图像超分辨率重建[J]. 激光与光电子学进展, 2017, 54(11): 111005.

    Wang M, Liu K X, Liu L, et al. Super-resolution reconstruction of image based on optimized convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111005.

[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] Hinton GE, SrivastavaN, KrizhevskyA, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. arXiv preprint arXiv: 1207. 0580, 2012.

[14] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770- 778.

[15] HuangG, LiuZ, Maaten L V D, et al. Densely connected convolutional networks[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2261- 2269.

[16] W3C Recommendation. Ink makeup language[EB/OL].( 2017-04-06)[2018-08-06]. http:∥www.w3.org/TR/InkML/.

[17] Simard PY, SteinkrausD, Platt JC. Bestpractices for convolutional neural networks applied to visual document analysis[C]∥International Conference on Document Analysis and Recognition, 2003: 958- 963.

[18] HuJ, ShenL, SunG. Squeeze-and-excitation networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132- 7141.

[19] IoffeS, SzegedyC. Batchnormalization: accelerating deep network training by reducing internal covariate shift[C]∥32 nd International Conference on Machine Learning , 2015, 37: 448- 456.

[20] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[21] Mouchère H, Zanibbi R, Garain U, et al. Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011-2014[J]. International Journal on Document Analysis and Recognition, 2016, 19(2): 173-189.

[22] MouchèreH, Viard-GaudinC, ZanibbiR, et al. ICFHR 2014 competition on recognition of on-line handwritten mathematical expressions (CROHME 2014)[C]∥International Conference on Frontiers in Handwriting Recognition, 2014: 791- 796.

[23] Kingma DP, Ba J. Adam: a method for stochastic optimization[J]. arXiv preprint arXiv: 1412. 6980, 2014.

方定邦, 冯桂, 曹海燕, 杨恒杰, 韩雪, 易银城. 基于多特征卷积神经网络的手写公式符号识别[J]. 激光与光电子学进展, 2019, 56(7): 072001. Dingbang Fang, Gui Feng, Haiyan Cao, Hengjie Yang, Xue Han, Yincheng Yi. Handwritten Formula Symbol Recognition Based on Multi-Feature Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(7): 072001.

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