{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:37:52Z","timestamp":1772044672361,"version":"3.50.1"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Asian\u00a0indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages have attained important internet amongst the research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low-resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is the most ancient Indian language that is mostly exploited in the Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning-based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in the Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs a capsule network (CapsNet) model for feature vector generation with the HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.<\/jats:p>","DOI":"10.1145\/3632173","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T11:46:53Z","timestamp":1699616813000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Asian Indigenous Language Processing through Deep Learning-based Handwriting Recognition and Optimization Techniques"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5671-9466","authenticated-orcid":false,"given":"A.","family":"Manimaran","sequence":"first","affiliation":[{"name":"School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-0119","authenticated-orcid":false,"given":"Mohammad Haider","family":"Syed","sequence":"additional","affiliation":[{"name":"Department of Computer science, Saudi Electronic University, Dammam, Kingdom of Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2502-5629","authenticated-orcid":false,"given":"M. Siva","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2652-8236","authenticated-orcid":false,"given":"S.","family":"Selvanayaki","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Saveetha Engineering College, Chennai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3305-8167","authenticated-orcid":false,"given":"Gurram","family":"Sunitha","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence &amp; Machine Learning, School of Computing, Mohan Babu University, Tirupati, A. P., India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6257-8463","authenticated-orcid":false,"given":"Asmita","family":"Manna","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India"}]}],"member":"320","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"2019 International Conference on Vision Towards Emerging Trends in Communication and Networking","author":"Pragathi M. A.","year":"2019","unstructured":"M. A. Pragathi, K. Priyadarshini, S. Saveetha, A. S. Banu, and K. M. Aarif. 2019. Handwritten tamil character recognition UsingDeep learning. In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). 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