International Journal on Science and Technology

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Document Template Matching using AI/ML

Author(s) Dr.Nasreen Fathima, Sanjay Waugh, Mohammed Safee, Mohammed Muzamil, Mohammed Kaleem
Country India
Abstract This study explores a robust system for automating document classification and structured data extraction using Optical Character Recognition (OCR). The proposed solution harnesses OCR technology to accurately identify and match document templates by analyzing the layout, structure, and textual content of scanned or digital documents. By comparing these features against a predefined set of templates, the system enables efficient handling of documents such as invoices, forms, and reports, significantly reducing manual intervention and improving accuracy. Key elements of the system include preprocessing methods to optimize OCR performance, template creation based on unique document attributes, and a matching algorithm for text and layout patterns. Designed for scalability and adaptability, this system addresses challenges such as noisy scanned images, diverse document formats, and inconsistencies in text recognition. It offers a reliable and efficient framework for real-time document processing, making it suitable for industries like healthcare, finance, and logistics where streamlined document management is essential.
Keywords OCR, Naïve Bayes, Classification, Pattern Matching, OpenCV
Field Computer > Design
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-31
Cite This Document Template Matching using AI/ML - Dr.Nasreen Fathima, Sanjay Waugh, Mohammed Safee, Mohammed Muzamil, Mohammed Kaleem - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2828
DOI https://doi.org/10.71097/IJSAT.v16.i1.2828
Short DOI https://doi.org/g9dgp4
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