Deep Learning in Archiving Indus Script and Motif Information.
This work presents a novel computational system for the automated digitization of image-based data from seals of the ancient Indus Valley Civilization (IVC). The objective of this system's design is to automatically extract and archive key information from seals or images, including the script and m...
| Publicado en: | Journal of Computer Applications in Archaeology Vol. 8; no. 1; pp. 156 - 170 |
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| Autores principales: | , , , , , |
| Formato: | Conference Paper/Materials |
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Ubiquity Press
2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191357204&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191357204 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 25148362 MPQT jtl: Journal of Computer Applications in Archaeology issn: 25148362 maglogo: N pubinfo: dt: 2025 vid: 8 iid: 1 pid: 83901 pub: Ubiquity Press artinfo: ui: 191357204 10.5334/jcaa.175 ppf: 156 ppct: 14 formats: tig: atl: Deep Learning in Archiving Indus Script and Motif Information. aug: au: Dixit, Vaishnavi Hussain, Nushrat Basak, Shubham Atturu, Deva Mitra, Debasis Bhattacharya, Ujjwal affil: Florida Institute of Technology, Melbourne, Florida, United States Indian Statistical Institute, Kolkata, India su: Deep learning Indus civilization Image processing Ancient civilization Machine learning Pattern perception Database management Digitization Indus River sug: subj: Indus River Deep learning Indus civilization Image processing Ancient civilization Machine learning Pattern perception Database management Digitization keyword: automated motif identification automated script recognition deep learning Indus Valley script information archival machine learning ab: This work presents a novel computational system for the automated digitization of image-based data from seals of the ancient Indus Valley Civilization (IVC). The objective of this system's design is to automatically extract and archive key information from seals or images, including the script and motifs. The system operates as a pipeline comprising three deep learning models integrated with a custom-designed database. Two models form the Ancient Script Recognition network (ASR-net), which digitizes sequences of graphemes from Indus seals, similar to Optical Character Recognition for modern languages. The third model, the Motif Identification network (MI-net), identifies recurring motifs—distinctive symbols or iconographic elements with specific functional significance in the IVC. The database stores the extracted information, linking it to the respective seal images in a structured format. This end-to-end pipeline has been fully implemented, from image input to database archival. The overarching aim of this work is to support the application of automated statistical methods in the ongoing efforts to decipher the Indus script. pubtype: Academic Journal doctype: Conference Paper/Materials src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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