{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:24:16Z","timestamp":1742923456684,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030570576"},{"type":"electronic","value":"9783030570583"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-57058-3_5","type":"book-chapter","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T23:06:05Z","timestamp":1597359965000},"page":"60-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ALEC: An Accurate, Light and Efficient Network for CAPTCHA Recognition"],"prefix":"10.1007","author":[{"given":"Nan","family":"Li","sequence":"first","affiliation":[]},{"given":"Qianyi","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Song","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265\u2013283 (2016)","key":"5_CR1"},{"unstructured":"El Ahmad, A.S., Yan, J., Tayara, M.: The robustness of Google CAPTCHA\u2019s. Computing Science, Newcastle University (2011)","key":"5_CR2"},{"doi-asserted-by":"crossref","unstructured":"El Ahmad, A.S., Yan, J., Marshall, L.: The robustness of a new captcha. In: Proceedings of the Third European Workshop on System Security, pp. 36\u201341. ACM (2010)","key":"5_CR3","DOI":"10.1145\/1752046.1752052"},{"doi-asserted-by":"crossref","unstructured":"Garg, G., Pollett, C.: Neural network captcha crackers. In: 2016 Future Technologies Conference (FTC), pp. 853\u2013861. IEEE (2016)","key":"5_CR4","DOI":"10.1109\/FTC.2016.7821703"},{"doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369\u2013376. ACM (2006)","key":"5_CR5","DOI":"10.1145\/1143844.1143891"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"5_CR6","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Hou, L., Yao, Q., Kwok, J.T.: Loss-aware binarization of deep networks. arXiv preprint arXiv:1611.01600 (2016)","key":"5_CR7"},{"unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)","key":"5_CR8"},{"issue":"2","key":"5_CR9","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s11042-009-0341-5","volume":"48","author":"S-Y Huang","year":"2010","unstructured":"Huang, S.-Y., Lee, Y.-K., Bell, G., Zhan-he, O.: An efficient segmentation algorithm for captchas with line cluttering and character warping. Multimedia Tools Appl. 48(2), 267\u2013289 (2010)","journal-title":"Multimedia Tools Appl."},{"doi-asserted-by":"crossref","unstructured":"Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530 (2015)","key":"5_CR10","DOI":"10.14257\/astl.2016.140.36"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)","key":"5_CR11"},{"doi-asserted-by":"crossref","unstructured":"Le, T.A., Baydin, A.G., Zinkov, R., Wood, F.: Using synthetic data to train neural networks is model-based reasoning. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3514\u20133521. IEEE (2017)","key":"5_CR12","DOI":"10.1109\/IJCNN.2017.7966298"},{"unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)","key":"5_CR13"},{"key":"5_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"issue":"18","key":"5_CR15","doi-asserted-by":"publisher","first-page":"3995","DOI":"10.1002\/sec.1316","volume":"8","author":"R Al Nachar","year":"2015","unstructured":"Al Nachar, R., Inaty, E., Bonnin, P.J., Alayli, Y.: Breaking down captcha using edge corners and fuzzy logic segmentation\/recognition technique. Secur. Commun. Netw. 8(18), 3995\u20134012 (2015)","journal-title":"Secur. Commun. Netw."},{"doi-asserted-by":"crossref","unstructured":"Qing, K., Zhang, R.: A multi-label neural network approach to solving connected captchas. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1313\u20131317. IEEE (2017)","key":"5_CR16","DOI":"10.1109\/ICDAR.2017.216"},{"doi-asserted-by":"crossref","unstructured":"Rui, C., Jing, Y., Hu, R., Huang, S.: A novel LSTM-RNN decoding algorithm in captcha recognition. In: 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 766\u2013771. IEEE (2013)","key":"5_CR17","DOI":"10.1109\/IMCCC.2013.171"},{"doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","key":"5_CR18","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"11","key":"5_CR19","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","volume":"39","author":"B Shi","year":"2015","unstructured":"Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298\u20132304 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Sifre, L., Mallat, S.: Rigid-motion scattering for image classification. Ph. D. dissertation (2014)","key":"5_CR20"},{"issue":"2","key":"5_CR21","first-page":"2242","volume":"5","author":"VP Singh","year":"2014","unstructured":"Singh, V.P., Pal, P.: Survey of different types of captcha. Int. J. Comput. Sci. Inf. Technol. 5(2), 2242\u20132245 (2014)","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","key":"5_CR22","DOI":"10.1109\/CVPR.2015.7298594"},{"unstructured":"Tulloch, A., Jia, Y.: High performance ultra-low-precision convolutions on mobile devices. arXiv preprint arXiv:1712.02427 (2017)","key":"5_CR23"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/3-540-39200-9_18","volume-title":"Advances in Cryptology \u2014 EUROCRYPT 2003","author":"L von Ahn","year":"2003","unstructured":"von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: using hard AI problems for security. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 294\u2013311. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-39200-9_18"},{"issue":"5","key":"5_CR25","doi-asserted-by":"publisher","first-page":"5851","DOI":"10.3934\/mbe.2019292","volume":"16","author":"J Wang","year":"2019","unstructured":"Wang, J., Qin, J.H., Xiang, X.Y., Tan, Y., Pan, N.: Captcha recognition based on deep convolutional neural network. Math. Biosci. Eng 16(5), 5851\u20135861 (2019)","journal-title":"Math. Biosci. Eng"},{"key":"5_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"doi-asserted-by":"crossref","unstructured":"Yan, J., El Ahmad, A.S.: Breaking visual captchas with Naive pattern recognition algorithms. In: Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007), pp. 279\u2013291. IEEE (2007)","key":"5_CR27","DOI":"10.1109\/ACSAC.2007.47"},{"doi-asserted-by":"crossref","unstructured":"Yan, J., El Ahmad, A.S.: A low-cost attack on a Microsoft captcha. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 543\u2013554. ACM (2008)","key":"5_CR28","DOI":"10.1145\/1455770.1455839"},{"issue":"7","key":"5_CR29","first-page":"891","volume":"37","author":"L Zhang","year":"2011","unstructured":"Zhang, L., Zhang, L., Huang, S.G., Shi, Z.X.: A highly reliable captcha recognition algorithm based on rejection. Acta Automatica Sinica 37(7), 891\u2013900 (2011)","journal-title":"Acta Automatica Sinica"},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","key":"5_CR30","DOI":"10.1109\/CVPR.2018.00716"},{"unstructured":"Zhu, M., Gupta, S.: To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)","key":"5_CR31"}],"container-title":["Lecture Notes in Computer Science","Document Analysis Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57058-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:04:05Z","timestamp":1723593845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-57058-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030570576","9783030570583"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57058-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Document Analysis Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"das2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iapr.org\/das2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.86","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.01","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic the conference was held as a virtual event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}