{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:58:08Z","timestamp":1760709488682,"version":"3.41.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319716060"},{"type":"electronic","value":"9783319716077"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-71607-7_14","type":"book-chapter","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T01:30:23Z","timestamp":1514511023000},"page":"149-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Recognition of Offline Handwritten Mathematical Symbols Using Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0267-9905","authenticated-orcid":false,"given":"Lanfang","family":"Dong","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7470-9077","authenticated-orcid":false,"given":"Hanchao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,12,30]]},"reference":[{"issue":"2","key":"14_CR1","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s10032-016-0263-5","volume":"19","author":"H Mouch\u00e8re","year":"2016","unstructured":"Mouch\u00e8re, H., Zanibbi, R., Garain, U., et al.: Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011\u20132014. Int. J. Doc. Anal. Recogn. 19(2), 173\u2013189 (2016)","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Mouchere, H., Viard-Gaudin, C., Zanibbi, R., et al.: ICFHR 2014 competition on recognition of on-line handwritten mathematical expressions (CROHME 2014). In: 14th International Conference on Frontiers in Handwriting Recognition, pp. 791\u2013796. IEEE Press, Crete (2014)","DOI":"10.1109\/ICFHR.2014.138"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Mouch\u00e8re, H., Viard-Gaudin, C., Zanibbi, R., et al.: ICFHR 2016 CROHME: competition on recognition of online handwritten mathematical expressions. In: 15th International Conference on Frontiers in Handwriting Recognition, Shenzhen, pp. 607\u2013612 (2016)","DOI":"10.1109\/ICFHR.2016.0116"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"\u00c1lvaro, F., S\u00e1nchez, J.A., Bened\u00ed, J.M.: Offline features for classifying handwritten math symbols with recurrent neural networks. In: 2014 22nd International Conference on Pattern Recognition, pp. 2944\u20132949. IEEE Press, Stockholm (2014)","DOI":"10.1109\/ICPR.2014.507"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Dai, N.H., Le, A.D., Nakagawa, M.: Deep neural networks for recognizing online handwritten mathematical symbols. In: 2015 3rd IAPR Asian Conference on Pattern Recognition, pp. 121\u2013125. IEEE Press, Kuala Lumpur (2015)","DOI":"10.1109\/ACPR.2015.7486478"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Davila, K., Ludi, S., Zanibbi, R.: Using off-line features and synthetic data for on-line handwritten math symbol recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 323\u2013328. IEEE Press, Crete (2014)","DOI":"10.1109\/ICFHR.2014.61"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Ramadhan, I., Purnama, B., Al, F.S.: Convolutional neural networks applied to handwritten mathematical symbols classification. In: 2016 4th International Conference on Information and Communication Technology, pp. 1\u20134. IEEE Press, Bandung (2016)","DOI":"10.1109\/ICoICT.2016.7571941"},{"issue":"11","key":"14_CR8","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"14_CR9","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, pp. 1097\u20131105 (2012)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, Boston, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"14_CR11","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., et al.: Mask R-CNN (2017). arXiv:1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"key":"14_CR15","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167"},{"key":"14_CR16","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network (2013). arXiv:1312.4400"},{"key":"14_CR17","unstructured":"Thoma, M.: The HASYv2 dataset (2017). arXiv:1701.08380"},{"key":"14_CR18","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., et al.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and\u00a0<\u00a00.5\u00a0MB model size (2016). arXiv:1602.07360"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"14_CR20","unstructured":"Ink markup language. http:\/\/www.w3.org\/TR\/InkML\/. Accessed 06 Apr 2017"},{"key":"14_CR21","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv:1207.0580"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: 2003 International Conference on Document Analysis and Recognition, Edinburgh, vol. 3, pp. 958\u2013962 (2003)","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Proceedings of the 22nd International Conference on Multimedia, Orlando, pp. 675\u2013678 (2014)","DOI":"10.1145\/2647868.2654889"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-71607-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,29]],"date-time":"2025-06-29T08:46:09Z","timestamp":1751186769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-71607-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319716060","9783319716077"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-71607-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"30 December 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/10times.com\/icig-sa","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}