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@article{195467,
        author = {F Ameer Althaf and Ms Abinaya S B.Sc.,MCA.,},
        title = {Fake ID / Document Dectection Detect Forged ID Using OCR and AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {682-687},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195467},
        abstract = {The rapid digitization of identity verification processes has created both opportunities and challenges in ensuring document authenticity. Forged identification documents remain a significant threat to security, financial institutions, and governance systems worldwide. This paper presents an AI-driven framework for detecting counterfeit IDs by integrating Optical Character Recognition (OCR) with advanced machine learning techniques.
OCR is employed to extract textual and structural features from identity documents, while deep learning models analyze inconsistencies in fonts, layouts, and embedded security elements. The proposed system leverages image prepossessing, feature engineering, and anomaly detection to identify subtle manipulations that are often overlooked by manual inspection. Experimental results demonstrate that the hybrid approach achieves high accuracy in distinguishing genuine documents from forged ones, outperforming traditional rule-based methods.
By automating forgery detection, this research contributes to strengthening digital trust, reducing fraud, and enhancing security in identity verification systems. This version balances technical detail with readability, making it suitable for publication.},
        keywords = {Finding fake IDs and finding forged documents OCR, or Optical Character Recognition AI, or artificial intelligence, Machine learning, deep learning, image processing, identity verification, and fraud detection are all parts of this field.},
        month = {April},
        }