Our inspiration came from the increasing use of fake identity documents and digital fraud. We wanted to build a smart, AI-powered solution that could help identify suspicious documents and make verification faster, more transparent, and reliable Document Scanning: Users upload an identity document for screening. OCR Extraction: Extracts information such as name, ID number, and date of birth. Forgery Detection: Checks for suspicious visual changes, layout inconsistencies, and unusual document patterns. Cross-Document Matching: Compares information across multiple submitted documents to identify mismatches. QR Code Verification: Checks QR codes and available digital verification information. Risk Assessment: Generates a risk classification (Low Risk, Review, or High Risk) with explanations of detected issues.
We developed an AI-based document screening platform using OCR, Python, OpenCV, and machine learning concepts. Our system extracts document information, checks visual inconsistencies, compares details across documents, and generates an explainable risk assessment through an interactive dashboard.
Some of our biggest challenges included implementing accurate OCR, identifying document anomalies, handling different document formats, connecting frontend and backend services, and developing a working prototype using free tools and resources.
Throughout this journey, we learned how to integrate AI with real-world applications, work with different technologies, develop an interactive user interface, and collaborate as a team. We also understood the importance of explainable AI and responsible document verification.
Built With
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- css
- html
- javascript
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- python
- react
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