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Distributed in myriad formats digital
documents proliferate — advanced tools for efficient and
reliable processing are in demand. Traditional Optical
Character Recognition (OCR) systems are indeed useful,
but can be extremely limited in their usage, in covering
complex types of document, or in poor scanning quality
inputs. Furthermore, much of textual content
summarizing from these documents remains manual, thus
creating inefficiencies for a tasks that could easily be
automated. In this paper, we present an integrated method
combining augmented OCR techniques with Google
Gemini’s generative artificial intelligence to automatically
extract and summarize text. The system also uses
advanced OCR algorithms to greatly improve text
recognition accuracy for poor quality scans and skewed
layouts. At the same time, generative AI models enable
concise and relevant summaries to be produced
simultaneously, with overall document processing
effectiveness and accessibility improved. Rigorous testing
on a broad suite of document types demonstrates how
much more effective the system is than traditional methods
in terms of accuracy and usability.
Keywords:
Optical Character Recognition (OCR), Generative Artificial Intelligence, Document Processing, Text Summarization, Machine Learning,
Cite Article:
"Automated Document Processing: Combining OCR and Generative AI for Efficient Text Extraction and Summarization", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c109-c114, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503220.pdf
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ISSN:
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