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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
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| Paper Title: | OCR-Integrated Retrieval-Augmented Generation for Intelligent PDF Processing |
| Authors Name: | Eshani Patel , Yash Deep Singh Bais , Liza Patel |
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IJRTI_212689
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| Published Paper Id: | IJRTI2605099 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | The pervasive digitization of institutional records has generated an extensive corpus of scanned, image-encoded PDF documents whose informational content remains inaccessible to conventional text-processing infrastructure. This paper presents and rigorously evaluates an integrated pipeline that unifies multi-engine Optical Character Recognition with semantic retrieval and large language model generation to enable natural language querying over arbitrary PDF documents. The proposed architecture deploys three parallel OCR engines—Tesseract 5.3, EasyOCR 1.7, and PaddleOCR 2.7—with quantitative output selection, followed by LangChain-orchestrated semantic chunking, 384-dimensional dense embedding via the all-MiniLM-L6-v2 sentence transformer, FAISS-indexed vector storage, and Retrieval-Augmented Generation inference over both locally deployed and API-backed large language models. Evaluation across a 42-document, 318-page heterogeneous corpus stratified into three document quality categories yields a FAISS Precision@5 of 0.78 and a Mean Reciprocal Rank of 0.86—representing a 17-percentage-point improvement over TF-IDF baselines. Human-assessed response correctness reached 74% under GPT-3.5-Turbo and 58% under the locally deployed Falcon-RW-1B model, at a mean query latency of 9.5 seconds. PaddleOCR achieved the lowest Word Error Rate of 9.7% on degraded documents, while Tesseract retains a marginal advantage on clean typeset material. The work provides a rigorous framework for OCR engine selection within retrieval-augmented pipelines—a design dimension absent from the prior integrated document intelligence literature. |
| Keywords: | Optical Character Recognition; Retrieval-Augmented Generation; Document Intelligence; FAISS; Semantic Embeddings; LangChain; PDF Processing; Large Language Models |
| Cite Article: | "OCR-Integrated Retrieval-Augmented Generation for Intelligent PDF Processing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a796-a806, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605099.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605099
Registration ID:212689
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a796-a806 Country: Raipur, Chhattisgarh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605099 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605099 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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