WRAP: An AI-Enhanced Visual Task Management Application with On-Device Automatic Tag Generation
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901206231678976 |
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| author | Senthil Kumar V Kaushiik Suhail Kavin Mohan Kumar Nikitha Magesh |
| author_facet | Senthil Kumar V Kaushiik Suhail Kavin Mohan Kumar Nikitha Magesh |
| contents | Abstract - Traditional task management applications primarily rely on text-based task entries and manual categorization, which often leads to inconsistent organization and limited search efficiency. This paper presents WRAP, an AI-enhanced visual task management application that integrates document attachment support and intelligent on-device automatic tagging. Unlike conventional to-do list applications, WRAP enables users to create tasks with images and documents while automatically generating relevant tags using Natural Language Processing techniques. The system employs Google ML Kit Entity Extraction combined with heuristic keyword analysis to identify meaningful contextual tags directly on the device without requiring cloud-based services. The application follows a layered architecture consisting of presentation, application, data, and file storage layers. Generated tags enhance task discoverability, improve search precision, and reduce manual categorization effort. Experimental evaluation demonstrates improved contextual organization and retrieval efficiency compared to manual tagging approaches. WRAP highlights the practical integration of on-device Artificial Intelligence techniques in modern productivity applications. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19914836 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | WRAP: An AI-Enhanced Visual Task Management Application with On-Device Automatic Tag Generation Senthil Kumar V Kaushiik Suhail Kavin Mohan Kumar Nikitha Magesh WRAP Task Management System Natural Language Processing ML Kit Automatic Tagging Android Application Semantic Analysis Abstract - Traditional task management applications primarily rely on text-based task entries and manual categorization, which often leads to inconsistent organization and limited search efficiency. This paper presents WRAP, an AI-enhanced visual task management application that integrates document attachment support and intelligent on-device automatic tagging. Unlike conventional to-do list applications, WRAP enables users to create tasks with images and documents while automatically generating relevant tags using Natural Language Processing techniques. The system employs Google ML Kit Entity Extraction combined with heuristic keyword analysis to identify meaningful contextual tags directly on the device without requiring cloud-based services. The application follows a layered architecture consisting of presentation, application, data, and file storage layers. Generated tags enhance task discoverability, improve search precision, and reduce manual categorization effort. Experimental evaluation demonstrates improved contextual organization and retrieval efficiency compared to manual tagging approaches. WRAP highlights the practical integration of on-device Artificial Intelligence techniques in modern productivity applications. |
| title | WRAP: An AI-Enhanced Visual Task Management Application with On-Device Automatic Tag Generation |
| topic | WRAP Task Management System Natural Language Processing ML Kit Automatic Tagging Android Application Semantic Analysis |
| url | https://doi.org/10.5281/zenodo.19914836 |