Generative AI-based translation tools suite
| dc.contributor.advisor | Rahman, Rafeed | |
| dc.contributor.author | Hossain, Ahbab | |
| dc.date.accessioned | 2026-04-21T05:35:11Z | |
| dc.date.available | 2026-04-21T05:35:11Z | |
| dc.date.issued | 2026-01 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (page 22). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2026. | |
| dc.description.abstract | This project presents the design and development of an intelligent document translation and preview system capable of handling multi-format files including Word, Excel, text, and images. The system integrates Optical Character Recognition (OCR), automated translation, and interactive preview functionalities within a unified interface. A dynamic tab-based architecture allows users to manage multiple translation sessions simultaneously, with real-time state management ensuring synchronization between the client and server. The system leverages modern large language models (LLMs), such as the Gemini API, to perform context-aware translations. To enhance efficiency, the backend implements asynchronous batching and batched API calls, minimizing latency during large-scale translation tasks. The chat assistant module provides contextual editing support and instruction-based translation refinement, allowing users to iteratively improve outputs without manual re-uploading. Advanced preview mechanisms have been implemented for each file type: formatted text display for Word, table parsing for Excel, OCR-based text extraction and alignment for images, and editable text containers for plain files. Together, these modules form a cohesive, scalable, and extensible framework for intelligent multilingual document processing. The result is a technically robust system that demonstrates a practical balance between automation, user control, and performance optimization | |
| dc.identifier.other | ID 24141201 | |
| dc.identifier.other | https://dspace.bracu.ac.bd/server/api/core/items/71654e0d-031a-46b3-9150-8b2ee7f8a223 | |
| dc.identifier.uri | http://hdl.handle.net/10361/27986 | |
| dc.language.iso | en | |
| dc.publisher | BRAC University | |
| dc.source | BRAC University Institutional Repository | |
| dc.subject | Document translation | |
| dc.subject | Large Language Models (LLMs) | |
| dc.subject | Gemini API | |
| dc.subject | Optical Character Recognition (OCR) | |
| dc.subject | Web application | |
| dc.title | Generative AI-based translation tools suite | |
| dc.type | Thesis |
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