{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T12:28:01Z","timestamp":1765974481423,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00521-023-08433-z","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T11:03:23Z","timestamp":1678359803000},"page":"12927-12943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Strike off removal in Indic scripts with transfer learning"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5393-1202","authenticated-orcid":false,"given":"Shivangi","family":"Nigam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adarsh Prasad","family":"Behera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manas","family":"Gogoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shekhar","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Nagabhushan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"8433_CR1","doi-asserted-by":"crossref","unstructured":"Adak C, Chaudhuri BB (2014) An approach of strike-through text identification from handwritten documents. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, IEEE, pp 643\u2013648","DOI":"10.1109\/ICFHR.2014.113"},{"issue":"108","key":"8433_CR2","first-page":"815","volume":"248","author":"AM Alvi","year":"2022","unstructured":"Alvi AM, Siuly S, Wang H et al (2022) A deep learning based framework for diagnosis of mild cognitive impairment. Knowl Based Syst 248(108):815","journal-title":"Knowl Based Syst"},{"key":"8433_CR3","doi-asserted-by":"crossref","unstructured":"Arlandis J, P\u00e9rez-Cortes JC, Cano J (2002) Rejection strategies and confidence measures for a k-nn classifier in an ocr task. In: Object recognition supported by user interaction for service robots, IEEE, pp 576\u2013579","DOI":"10.1109\/ICPR.2002.1044806"},{"key":"8433_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.clinimag.2022.11.003","volume":"94","author":"S Atasever","year":"2022","unstructured":"Atasever S, Azg\u0131noglu N, Terz\u0131 DS et al (2022) A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging 94:18\u201341","journal-title":"Clin Imaging"},{"key":"8433_CR5","doi-asserted-by":"crossref","unstructured":"Banerjee J, Namboodiri AM, Jawahar C (2009) Contextual restoration of severely degraded document images. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 517\u2013524","DOI":"10.1109\/CVPR.2009.5206601"},{"key":"8433_CR6","doi-asserted-by":"crossref","unstructured":"Bhattacharya U, Chaudhuri B (2005) Databases for research on recognition of handwritten characters of indian scripts. In: Eighth International Conference on Document Analysis and Recognition (ICDAR\u201905), IEEE, pp 789\u2013793","DOI":"10.1109\/ICDAR.2005.84"},{"key":"8433_CR7","doi-asserted-by":"crossref","unstructured":"Brink A, van\u00a0der Klauw H, Schomaker L (2008) Automatic removal of crossed-out handwritten text and the effect on writer verification and identification. In: Document Recognition and Retrieval XV, International Society for Optics and Photonics, p 68150A","DOI":"10.1117\/12.766466"},{"key":"8433_CR8","doi-asserted-by":"publisher","first-page":"e861","DOI":"10.7717\/peerj-cs.861","volume":"8","author":"M Eltay","year":"2022","unstructured":"Eltay M, Zidouri A, Ahmad I et al (2022) Generative adversarial network based adaptive data augmentation for handwritten arabic text recognition. Peer J Comput Sci 8:e861","journal-title":"Peer J Comput Sci"},{"key":"8433_CR9","doi-asserted-by":"crossref","unstructured":"Fogel S, Averbuch-Elor H, Cohen S, et\u00a0al (2020) Scrabblegan: Semi-supervised varying length handwritten text generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4324\u20134333","DOI":"10.1109\/CVPR42600.2020.00438"},{"key":"8433_CR10","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, PMLR, pp 1180\u20131189"},{"key":"8433_CR11","doi-asserted-by":"crossref","unstructured":"Gongidi S, Jawahar C (2021) iiit-indic-hw-words: A dataset for indic handwritten text recognition. In: International Conference on Document Analysis and Recognition, Springer, pp 444\u2013459","DOI":"10.1007\/978-3-030-86337-1_30"},{"key":"8433_CR12","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et\u00a0al (2014) Generative adversarial nets. Advances in neural information processing systems. 27"},{"key":"8433_CR13","doi-asserted-by":"crossref","unstructured":"Heil R, Vats E, Hast A (2021) Strikethrough removal from handwritten words using cyclegans. In: International Conference on Document Analysis and Recognition, Springer, pp 572\u2013586","DOI":"10.1007\/978-3-030-86337-1_38"},{"key":"8433_CR14","doi-asserted-by":"crossref","unstructured":"Heil R, Vats E, Hast A (2022) Paired image to image translation for strikethrough removal from handwritten words. arXiv preprint arXiv:2201.09633","DOI":"10.1007\/978-3-031-06555-2_21"},{"key":"8433_CR15","doi-asserted-by":"crossref","unstructured":"Huang JT, Li J, Yu D, et\u00a0al (2013) Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, pp 7304\u20137308","DOI":"10.1109\/ICASSP.2013.6639081"},{"issue":"3","key":"8433_CR16","doi-asserted-by":"publisher","first-page":"847","DOI":"10.3390\/s21030847","volume":"21","author":"J Kubicek","year":"2021","unstructured":"Kubicek J, Penhaker M, Krejcar O et al (2021) Modern trends and applications of intelligent methods in biomedical signal and image processing. Sensors 21(3):847","journal-title":"Sensors"},{"key":"8433_CR17","doi-asserted-by":"crossref","unstructured":"Liao M, Shi B, Bai X, et\u00a0al (2017) Textboxes: A fast text detector with a single deep neural network. In: Thirty-First AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v31i1.11196"},{"key":"8433_CR18","unstructured":"Long M, Cao Y, Wang J, et\u00a0al (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, PMLR, pp 97\u2013105"},{"key":"8433_CR19","unstructured":"Long M, Zhu H, Wang J, et\u00a0al (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning, PMLR, pp 2208\u20132217"},{"key":"8433_CR20","unstructured":"Luo Z, Zou Y, Hoffman J, et\u00a0al (2017) Label efficient learning of transferable representations acrosss domains and tasks. Advances in neural information processing systems. 30"},{"issue":"1","key":"8433_CR21","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"UV Marti","year":"2002","unstructured":"Marti UV, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recognit 5(1):39\u201346","journal-title":"Int J Doc Anal Recognit"},{"key":"8433_CR22","unstructured":"Nicolas S, Paquet T, Heutte L (2006) Markov random field models to extract the layout of complex handwritten documents. In: Tenth International Workshop on Frontiers in Handwriting Recognition, Suvisoft"},{"key":"8433_CR23","doi-asserted-by":"publisher","unstructured":"Nigam S, Behera A, Verma S, et\u00a0al (2022) Deformity removal from handwritten text documents using variable cycle gan. PREPRINT (Version 1) available at Research Square pp 1\u201316. https:\/\/doi.org\/10.21203\/rs.3.rs-1488498\/v1","DOI":"10.21203\/rs.3.rs-1488498\/v1"},{"key":"8433_CR24","doi-asserted-by":"crossref","unstructured":"Nisa H, Thom JA, Ciesielski V, et\u00a0al (2019) A deep learning approach to handwritten text recognition in the presence of struck-out text. In: 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE, pp 1\u20136","DOI":"10.1109\/IVCNZ48456.2019.8961024"},{"issue":"2","key":"8433_CR25","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","volume":"1","author":"S Niu","year":"2020","unstructured":"Niu S, Liu Y, Wang J et al (2020) A decade survey of transfer learning (2010\u20132020). IEEE Trans Artif Intell 1(2):151\u2013166","journal-title":"IEEE Trans Artif Intell"},{"key":"8433_CR26","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, et\u00a0al (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1717\u20131724","DOI":"10.1109\/CVPR.2014.222"},{"key":"8433_CR27","unstructured":"Pardoe D, Stone P (2010) Boosting for regression transfer. In: ICML"},{"key":"8433_CR28","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621"},{"key":"8433_CR29","unstructured":"Pillalamarri A (2018) Evolution of script in india. https:\/\/journalsofindia.com\/evolution-of-script-in-india\/"},{"key":"8433_CR30","unstructured":"Pillalamarri A (2019) The story of india\u2019s many scripts. https:\/\/thediplomat.com\/2019\/07\/the-story-of-indias-many-scripts\/"},{"key":"8433_CR31","doi-asserted-by":"crossref","unstructured":"Poddar A, Chakraborty A, Mukhopadhyay J, et\u00a0al (2021a) Detection and localisation of struck-out-strokes in handwritten manuscripts. In: International Conference on Document Analysis and Recognition, Springer, pp 98\u2013112","DOI":"10.1007\/978-3-030-86159-9_7"},{"key":"8433_CR32","doi-asserted-by":"crossref","unstructured":"Poddar A, Chakraborty A, Mukhopadhyay J, et\u00a0al (2021b) Texrgan: a deep adversarial framework for text restoration from deformed handwritten documents. In: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, pp 1\u20139","DOI":"10.1145\/3490035.3490306"},{"key":"8433_CR33","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.asoc.2016.12.048","volume":"53","author":"Z Rezaei","year":"2017","unstructured":"Rezaei Z, Selamat A, Taki A et al (2017) Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images. Appl Soft Comput 53:380\u2013395","journal-title":"Appl Soft Comput"},{"key":"8433_CR34","unstructured":"Rosenstein MT (2005) To transfer or not to transfer. In: NIPS 2005"},{"issue":"1","key":"8433_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00125-5","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki R, Ahmed K, Wang H et al (2020) Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst 8(1):1\u20139","journal-title":"Health Inf Sci Syst"},{"key":"8433_CR36","doi-asserted-by":"crossref","unstructured":"Shonenkov A, Karachev D, Novopoltsev M, et\u00a0al (2021) Handwritten text generation and strikethrough characters augmentation. arXiv preprint arXiv:2112.07395","DOI":"10.18287\/2412-6179-CO-1049"},{"issue":"1","key":"8433_CR37","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MAHC.2009.1","volume":"31","author":"RMK Sinha","year":"2009","unstructured":"Sinha RMK (2009) A journey from indian scripts processing to indian language processing. IEEE Ann Hist Comput 31(1):8\u201331","journal-title":"IEEE Ann Hist Comput"},{"key":"8433_CR38","doi-asserted-by":"crossref","unstructured":"Tan C, Sun F, Kong T, et\u00a0al (2018) A survey on deep transfer learning. In: International conference on artificial neural networks, Springer, pp 270\u2013279","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"8433_CR39","unstructured":"Tzeng E, Hoffman J, Zhang N, et\u00a0al (2014) Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474"},{"issue":"102","key":"8433_CR40","first-page":"013","volume":"95","author":"JC Vizcarra","year":"2022","unstructured":"Vizcarra JC, Burlingame EA, Hug CB et al (2022) A community-based approach to image analysis of cells, tissues and tumors. Comput Med Imaging Gr 95(102):013","journal-title":"Comput Med Imaging Gr"},{"issue":"3","key":"8433_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT et al (2020) Generalizing from a few examples: A survey on few-shot learning. ACM Comput Surv (CSUR) 53(3):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"1","key":"8433_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big data 3(1):1\u201340","journal-title":"J Big data"},{"key":"8433_CR43","doi-asserted-by":"crossref","unstructured":"Wigington C, Stewart S, Davis B, et\u00a0al (2017) Data augmentation for recognition of handwritten words and lines using a cnn-lstm network. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp 639\u2013645","DOI":"10.1109\/ICDAR.2017.110"},{"issue":"9","key":"8433_CR44","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian Y, Lampert CH, Schiele B et al (2018) Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell 41(9):2251\u20132265","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"8433_CR45","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TKDE.2017.2669193","volume":"29","author":"Y Xu","year":"2017","unstructured":"Xu Y, Pan SJ, Xiong H et al (2017) A unified framework for metric transfer learning. IEEE Trans Knowl Data Eng 29(6):1158\u20131171","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8433_CR46","doi-asserted-by":"crossref","unstructured":"Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 1855\u20131862","DOI":"10.1109\/CVPR.2010.5539857"},{"key":"8433_CR47","doi-asserted-by":"crossref","unstructured":"Zhu H, Long M, Wang J, et\u00a0al (2016) Deep hashing network for efficient similarity retrieval. In: Proceedings of the AAAI conference on Artificial Intelligence","DOI":"10.1609\/aaai.v30i1.10235"},{"key":"8433_CR48","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, et\u00a0al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08433-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08433-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08433-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:43:08Z","timestamp":1683826988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08433-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":48,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["8433"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08433-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,3,9]]},"assertion":[{"value":"4 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors have agreed with the content and give explicit consent to submit the work. All authors have obtained consent from the responsible authorities at the institute\/organization where the work has been carried out.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}