<![CDATA[Institutional documents in vocational higher education are generally stored in PDF format with heterogeneous characteristics, including both digital and scanned documents, making the manual information retrieval process less efficient. This study aims to develop an OCR-Integrated Retrieval-Augmented Generation (RAG) system to support PDF-document question answering that can provide accurate and traceable document-based answers. The proposed framework consists of three main stages such as document ingestion and processing, retrieval-augmented question answering, and evaluation. The system integrates text extraction using PyMuPDF, Google Tesseract (v5.3.3) as a OCR fallback for the documents, text cleaning, chunking, embedding formation, vector storage with Facebook AI Similarity Search (FAISS), semantic retrieval, prompt augmentation, and answer generation. Evaluation is carried out using Retrieval Augmented Generation Assessment (RAGAS) Evaluation, BERTScore F1 and Bilingual Evaluation Understudy (BLEU) Score. This experiment using 50 total of PDF-documents related with various institutional documents from Vocational Higher Education with 150 test question-answer queries. The results show that the Multilingual-e5-base embedding model has best performed with a context precision of 0.8333, a faithfulness score of 1.0000 and the BERTScore F1 score reached 0.6494. These research indicate that OCR-Integration plays a critical role in enabling the RAG pipeline to improves retrieval and answer-generation performance.]]>
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