Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant.

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Bibliographic Details
Title:	Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant.
Authors:	Wijaya, Ryan Satria¹ ryan@polibatam.ac.id, Fahreji, Rival¹ rivalfahreji91@gmail.com
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Apr2026, Vol. 16 Issue 2, p1002-1009. 8p.
Subjects:	Optical character recognition, Multimodal user interfaces, Chatbots, Information retrieval, Intelligent personal assistants, Educational resources, Natural language processing, Application program interfaces
Abstract:	Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v16i2.pp1002-1009