{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:38:36Z","timestamp":1758044316492,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032046130","type":"print"},{"value":"9783032046147","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T00:00:00Z","timestamp":1757721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T00:00:00Z","timestamp":1757721600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04614-7_7","type":"book-chapter","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:24:34Z","timestamp":1757679874000},"page":"115-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Scribbles to\u00a0Text: A Novel Transformer-Based Recognition Model for\u00a0Child Handwriting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1210-0124","authenticated-orcid":false,"given":"Sahana","family":"Rangasrinivasan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7135-391X","authenticated-orcid":false,"given":"Sumi Suresh","family":"M. S.","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7118-9280","authenticated-orcid":false,"given":"Srirangaraj","family":"Setlur","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0443-8794","authenticated-orcid":false,"given":"Bharat","family":"Jayaraman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5318-7409","authenticated-orcid":false,"given":"Venu","family":"Govindaraju","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,13]]},"reference":[{"key":"7_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"7_CR2","first-page":"42388","volume":"37","author":"R Baena","year":"2025","unstructured":"Baena, R., Kalleli, S., Aubry, M.: General detection-based text line recognition. Adv. Neural. Inf. Process. Syst. 37, 42388\u201342404 (2025)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Baggett, M., Diamond, L.L., Olszewski, A.: Dysgraphia and dyslexia indicators: analyzing children\u2019s writing. Int. School Clinic 59(5), 10534512231189449 (2023)","DOI":"10.1177\/10534512231189449"},{"key":"7_CR4","unstructured":"Bai, S., et\u00a0al.: Qwen2. 5-VL technical report. arXiv preprint arXiv:2502.13923 (2025)"},{"issue":"1","key":"7_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/diagnostics15010004","volume":"15","author":"S Benredjem","year":"2024","unstructured":"Benredjem, S., et al.: Parkinson\u2019s disease prediction: an attention-based multimodal fusion framework using handwriting and clinical data. Diagnostics 15(1), 4 (2024)","journal-title":"Diagnostics"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0989-3","volume":"19","author":"GD Cascarano","year":"2019","unstructured":"Cascarano, G.D., et al.: Biometric handwriting analysis to support Parkinson\u2019s disease assessment and grading. BMC Med. Inform. Decis. Mak. 19, 1\u201311 (2019)","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"Suppl 1","key":"7_CR7","doi-asserted-by":"publisher","first-page":"S46","DOI":"10.21037\/tp.2019.11.01","volume":"9","author":"PJ Chung","year":"2020","unstructured":"Chung, P.J., Patel, D.R., Nizami, I.: Disorder of written expression and dysgraphia: definition, diagnosis, and management. Trans. ped. 9(Suppl 1), S46 (2020)","journal-title":"Trans. ped."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.patrec.2018.05.013","volume":"121","author":"C De Stefano","year":"2019","unstructured":"De Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., di Freca, A.S.: Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recogn. Lett. 121, 37\u201345 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"7_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"21541","DOI":"10.1038\/s41598-020-78611-9","volume":"10","author":"P Drot\u00e1r","year":"2020","unstructured":"Drot\u00e1r, P., Dobe\u0161, M.: Dysgraphia detection through machine learning. Sci. Rep. 10(1), 21541 (2020)","journal-title":"Sci. Rep."},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Fujitake, M.: DTrOCR: decoder-only transformer for optical character recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 8025\u20138035 (2024)","DOI":"10.1109\/WACV57701.2024.00784"},{"issue":"3","key":"7_CR12","doi-asserted-by":"publisher","first-page":"381","DOI":"10.3390\/diagnostics15030381","volume":"15","author":"JD Gallo-Aristizabal","year":"2025","unstructured":"Gallo-Aristizabal, J.D., Escobar-Grisales, D., R\u00edos-Urrego, C.D., Vargas-Bonilla, J.F., Garc\u00eda, A.M., Orozco-Arroyave, J.R.: Towards Parkinson\u2019s disease detection through analysis of everyday handwriting. Diagnostics 15(3), 381 (2025)","journal-title":"Diagnostics"},{"key":"7_CR13","unstructured":"Garrido-Munoz, C., Rios-Vila, A., Calvo-Zaragoza, J.: Handwritten text recognition: a survey. arXiv preprint arXiv:2502.08417 (2025)"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Ghiriti, A., G\u00f6derle, W., Kern, R.: Exploring the capabilities of GPT4-vision as OCR engine. In: International Conference on Theory and Practice of Digital Libraries, pp. 3\u201312. Springer (2024)","DOI":"10.1007\/978-3-031-72440-4_1"},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","DOI":"10.1049\/htl2.70006","volume":"12","author":"NTN Ho","year":"2025","unstructured":"Ho, N.T.N., Gonzalez, P., Gogovi, G.K.: Writing the signs: an explainable machine learning approach for Alzheimer\u2019s disease classification from handwriting. Healthcare Technol. Lett. 12(1), e70006 (2025)","journal-title":"Healthcare Technol. Lett."},{"key":"7_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120740","volume":"231","author":"J Kunhoth","year":"2023","unstructured":"Kunhoth, J., Al Maadeed, S., Saleh, M., Akbari, Y.: CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children. Expert Syst. Appl. 231, 120740 (2023)","journal-title":"Expert Syst. Appl."},{"key":"7_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104715","volume":"83","author":"J Kunhoth","year":"2023","unstructured":"Kunhoth, J., Al Maadeed, S., Saleh, M., Akbari, Y.: Exploration and analysis of on-surface and in-air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods. Biomed. Signal Process. Control 83, 104715 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"7_CR18","unstructured":"Kunhoth, J., Al-Maadeed, S., Saleh, M., Akbari, Y.: Multimodal ensemble with conditional feature fusion for dysgraphia diagnosis in children from handwriting samples. arXiv preprint arXiv:2408.13754 (2024)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Li, M., et al.: TROCR: transformer-based optical character recognition with pre-trained models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 13094\u201313102 (2023)","DOI":"10.1609\/aaai.v37i11.26538"},{"key":"7_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110967","volume":"158","author":"Y Li","year":"2025","unstructured":"Li, Y., Chen, D., Tang, T., Shen, X.: HTR-VT: handwritten text recognition with vision transformer. Pattern Recogn. 158, 110967 (2025)","journal-title":"Pattern Recogn."},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Lozupone, G., Nardone, E., Pace, C.D., D\u2019Alessandro, T.: Transformers and CNNs in neurodiagnostics: handwriting analysis for Alzheimer\u2019s diagnosis. In: International Conference on Pattern Recognition, pp. 447\u2013463. Springer (2025)","DOI":"10.1007\/978-3-031-78195-7_30"},{"key":"7_CR22","unstructured":"Lv, T., et\u00a0al.: KOSMOS-2.5: a multimodal literate model. arXiv preprint arXiv:2309.11419 (2023)"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"UV Marti","year":"2002","unstructured":"Marti, U.V., Bunke, H.: The Iam-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5, 39\u201346 (2002)","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"7_CR24","unstructured":"Microsoft: Azure ai vision services. https:\/\/azure.microsoft.com\/en-us\/products\/ai-services\/ai-vision (2023). Accessed 15 June 2024"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Parres, D., Anitei, D., Paredes, R.: Handwritten document recognition using pre-trained vision transformers. In: International Conference on Document Analysis and Recognition, pp. 173\u2013190. Springer (2024)","DOI":"10.1007\/978-3-031-70536-6_11"},{"issue":"5","key":"7_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-025-03927-0","volume":"6","author":"S Rangasrinivasan","year":"2025","unstructured":"Rangasrinivasan, S., Suresh, S., Olszewski, A., Setlur, S., Jayaraman, B., Govindaraju, V.: Ai-enhanced child handwriting analysis: a framework for the early screening of dyslexia and dysgraphia. SN Comput. Sci. 6(5), 1\u201326 (2025)","journal-title":"SN Comput. Sci."},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Retsinas, G., Nikolaidou, K., Sfikas, G.: Enhancing CRNN HTR architectures with transformer blocks. In: International Conference on Document Analysis and Recognition, pp. 425\u2013440. Springer (2024)","DOI":"10.1007\/978-3-031-70546-5_25"},{"key":"7_CR28","unstructured":"Services, A.W.: Amazon textract. https:\/\/aws.amazon.com\/textract\/. Accessed 05 Nov 2024"},{"issue":"6","key":"7_CR29","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1002\/mdc3.12552","volume":"4","author":"M Thomas","year":"2017","unstructured":"Thomas, M., Lenka, A., Kumar Pal, P.: Handwriting analysis in Parkinson\u2019s disease: current status and future directions. Movem. Disorders Clinical Pract. 4(6), 806\u2013818 (2017)","journal-title":"Movem. Disorders Clinical Pract."},{"key":"7_CR30","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Info. Process. Syst. 30 (2017)"},{"key":"7_CR31","doi-asserted-by":"publisher","unstructured":"Xu, S., Pan, Z.: A novel ensemble of random forest for assisting diagnosis of Parkinson\u2019s disease on small handwritten dynamics dataset. Int. J. Med. Info. 144, 104283 (2020). https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104283, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1386505620306511","DOI":"10.1016\/j.ijmedinf.2020.104283"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04614-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:24:42Z","timestamp":1757679882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04614-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,13]]},"ISBN":["9783032046130","9783032046147"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04614-7_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,13]]},"assertion":[{"value":"13 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This material is based upon work supported under the AI Research Institutes program by the U.S. National Science Foundation and the Institute of Education Sciences, U.S. Department of Education, through Award # 2229873 - National AI Institute for Exceptional Education. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the Institute of Education Sciences, or the U.S. Department of Education.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}